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ceshine/t5-paraphrase-paws-msrp-opinosis
2021-02-22T09:43:18.000Z
[ "pytorch", "t5", "seq2seq", "en", "transformers", "paraphrasing", "paraphrase", "license:apache-2.0", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json" ]
ceshine
186
transformers
--- language: "en" tags: - t5 - paraphrasing - paraphrase license: "Apache-2.0" --- # T5-base Parapharasing model fine-tuned on PAWS, MSRP, and Opinosis More details in [ceshine/finetuning-t5 Github repo](https://github.com/ceshine/finetuning-t5/tree/master/paraphrase)
ceshine/t5-paraphrase-quora-paws
2021-02-21T10:56:14.000Z
[ "pytorch", "t5", "seq2seq", "en", "transformers", "paraphrasing", "paraphrase", "license:apache-2.0", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json" ]
ceshine
651
transformers
--- language: "en" tags: - t5 - paraphrasing - paraphrase license: "Apache-2.0" --- # T5-base Parapharasing model fine-tuned on PAWS and Quora More details in [ceshine/finetuning-t5 Github repo](https://github.com/ceshine/finetuning-t5/tree/master/paraphrase)
ceyda/wav2vec2-base-760-turkish
2021-04-06T23:50:00.000Z
[ "pytorch", "wav2vec2", "tr", "dataset:common_voice", "transformers", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "license:apache-2.0" ]
automatic-speech-recognition
[ ".gitattributes", "README.md", "config.json", "preprocessor_config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
ceyda
17
transformers
--- language: tr datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Wav2Vec2-Base Turkish by Ceyda Cinarel results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice tr type: common_voice args: tr metrics: - name: Test WER type: wer value: 22.60 --- # Wav2Vec2-Base-760-Turkish # TBA Pretrained Turkish model [ceyda/wav2vec2-base-760](https://huggingface.co/ceyda/wav2vec2-base-760). Fine-tuned on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("ceyda/wav2vec2-base-960-turkish") model = Wav2Vec2ForCTC.from_pretrained("ceyda/wav2vec2-base-960-turkish") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tr", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("ceyda/wav2vec2-base-960-turkish") model = Wav2Vec2ForCTC.from_pretrained("ceyda/wav2vec2-base-960-turkish") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays #Attention mask is not used because the base-model was not trained with it. reference: https://github.com/huggingface/transformers/blob/403d530eec105c0e229fc2b754afdf77a4439def/src/transformers/models/wav2vec2/tokenization_wav2vec2.py#L305 def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids,skip_special_tokens=True) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Results**: - WER: 22.602390 - CER: 6.054137 ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://github.com/cceyda/wav2vec2)
ceyda/wav2vec2-base-760
2021-04-23T12:03:37.000Z
[ "pytorch", "wav2vec2", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin" ]
ceyda
13
transformers
Pretrained on 720h~ of Turkish speech data TBA
ceyda/wav2vec2-large-xlsr-53-sakha
2021-03-30T19:11:16.000Z
[ "sah", "dataset:common_voice", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "license:apache-2.0" ]
automatic-speech-recognition
[ ".gitattributes", "README.md" ]
ceyda
0
--- language: sah datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Sakha by Ceyda Cinarel results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice sah type: common_voice args: sah metrics: - name: Test WER type: wer value: 39.00 --- # Wav2Vec2-Large-XLSR-53-Sakha This is just a rough experimental baseline. I suggest you search for other better models on the hub. Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Sakha using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "sah", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("ceyda/wav2vec2-large-xlsr-53-sakha") model = Wav2Vec2ForCTC.from_pretrained("ceyda/wav2vec2-large-xlsr-53-sakha") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Sakha test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "sah", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("ceyda/wav2vec2-large-xlsr-53-sakha") model = Wav2Vec2ForCTC.from_pretrained("ceyda/wav2vec2-large-xlsr-53-sakha") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\"\%\'\"\�\'\`…\]\[\&\'»«]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 39.00 % ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://github.com/cceyda/wav2vec2)
ceyda/wav2vec2-large-xlsr-53-turkish
2021-03-29T03:33:04.000Z
[ "pytorch", "wav2vec2", "tr", "dataset:common_voice", "transformers", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "license:apache-2.0" ]
automatic-speech-recognition
[ ".gitattributes", "README.md", "config.json", "preprocessor_config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
ceyda
18
transformers
--- language: tr datasets: - common_voice metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Turkish by Ceyda Cinarel results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice tr type: common_voice args: tr metrics: - name: Test WER type: wer value: 27.59 --- # Wav2Vec2-Large-XLSR-53-Turkish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Turkish using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "tr", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("ceyda/wav2vec2-large-xlsr-53-turkish") model = Wav2Vec2ForCTC.from_pretrained("ceyda/wav2vec2-large-xlsr-53-turkish") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Turkish test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "tr", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("ceyda/wav2vec2-large-xlsr-53-turkish") model = Wav2Vec2ForCTC.from_pretrained("ceyda/wav2vec2-large-xlsr-53-turkish") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\]\[\’»«]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 27.59 % ## Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://github.com/cceyda/wav2vec2)
chain/yoloyolo
2021-06-11T10:31:54.000Z
[]
[ ".gitattributes" ]
chain
0
chambliss/distilbert-for-food-extraction
2020-10-14T21:58:56.000Z
[ "pytorch", "tf", "distilbert", "token-classification", "transformers" ]
token-classification
[ ".gitattributes", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.txt", "distilbert-for-food-extraction/pytorch_model.bin", "distilbert-for-food-extraction/special_tokens_map.json", "distilbert-for-food-extraction/vocab.txt" ]
chambliss
455
transformers
charmys/Erika
2021-06-18T14:42:33.000Z
[]
[ ".gitattributes" ]
charmys
0
cheekyAM/test
2021-06-08T09:35:09.000Z
[]
[ ".gitattributes" ]
cheekyAM
0
chenqian/bert_cn_finetuning
2021-05-19T14:01:43.000Z
[ "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results.txt", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin" ]
chenqian
25
transformers
chenqian/bert_finetuning_test
2021-05-19T14:02:25.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results.txt", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
chenqian
20
transformers
chenqiren/Lang
2021-03-12T03:50:48.000Z
[]
[ ".gitattributes" ]
chenqiren
0
cheulyop/wav2vec2-large-xlsr-ksponspeech_1-20
2021-05-24T08:42:21.000Z
[ "pytorch", "wav2vec2", "transformers" ]
[ ".gitattributes", "config.json", "optimizer.pt", "preprocessor_config.json", "pytorch_model.bin", "scheduler.pt", "special_tokens_map.json", "tokenizer_config.json", "trainer_state.json", "training_args.bin", "vocab.json" ]
cheulyop
29
transformers
chiayewken/aspect-sentiment-pretrain
2021-05-19T14:03:18.000Z
[ "pytorch", "jax", "bert", "transformers" ]
[ ".gitattributes", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
chiayewken
13
transformers
chihao/bert_cn_finetuning
2021-05-19T14:04:26.000Z
[ "pytorch", "jax", "bert", "text-classification", "transformers" ]
text-classification
[ ".gitattributes", "config.json", "eval_results.txt", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
chihao
15
transformers
chimka/chimka1
2021-03-04T09:21:10.000Z
[]
[ ".gitattributes", "README.md" ]
chimka
0
chipmooon/test_model
2021-05-24T11:01:18.000Z
[]
[ ".gitattributes", "test" ]
chipmooon
0
chirag2706/gpt2_code_generation_model
2021-05-21T14:54:10.000Z
[ "pytorch", "jax", "gpt2", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "added_tokens.json", "config.json", "flax_model.msgpack", "gpt_sh_config.json", "log.out", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
chirag2706
34,402
transformers
chkla/roberta-argument
2021-05-20T15:19:04.000Z
[ "pytorch", "jax", "roberta", "text-classification", "english", "transformers" ]
text-classification
[ ".DS_Store", ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
chkla
170,136
transformers
--- language: english widget: - text: "It has been determined that the amount of greenhouse gases have decreased by almost half because of the prevalence in the utilization of nuclear power." --- ### Welcome to RoBERTArg! 🤖 **Model description** This model was trained on ~25k heterogeneous manually annotated sentences (📚 [Stab et al. 2018](https://www.aclweb.org/anthology/D18-1402/)) of controversial topics to classify text into one of two labels: 🏷 **NON-ARGUMENT** (0) and **ARGUMENT** (1). 🗃 **Dataset** The dataset (📚 Stab et al. 2018) consists of **ARGUMENTS** (\~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a **NON-ARGUMENT** (\~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include "an obvious polarity to the possible outcomes" and compile a final set of eight controversial topics: _abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage_. | TOPIC | ARGUMENT | NON-ARGUMENT | |----|----|----| | abortion | 2213 | 2,427 | | school uniforms | 325 | 1,734 | | death penalty | 325 | 2,083 | | marijuana legalization | 325 | 1,262 | | nuclear energy | 325 | 2,118 | | cloning | 325 | 1,494 | | gun control | 325 | 1,889 | | minimum wage | 325 | 1,346 | 🏃🏼‍♂️**Model training** **RoBERTArg** was fine-tuned on a RoBERTA (base) pre-trained model from HuggingFace using the HuggingFace trainer with the following hyperparameters: ``` training_args = TrainingArguments( num_train_epochs=2, learning_rate=2.3102e-06, seed=8, per_device_train_batch_size=64, per_device_eval_batch_size=64, ) ``` 📊 **Evaluation** The model was evaluated on an evaluation set (20%): | Model | Acc | F1 | R arg | R non | P arg | P non | |----|----|----|----|----|----|----| | RoBERTArg | 0.8193 | 0.8021 | 0.8463 | 0.7986 | 0.7623 | 0.8719 | Showing the **confusion matrix** using again the evaluation set: | | ARGUMENT | NON-ARGUMENT | |----|----|----| | ARGUMENT | 2213 | 558 | | NON-ARGUMENT | 325 | 1790 | ⚠️ **Intended Uses & Potential Limitations** The model can only be a starting point to dive into the exciting field of argument mining. But be aware. An argument is a complex structure, with multiple dependencies. Therefore, the model may perform less well on different topics and text types not included in the training set. Enjoy and stay tuned! 🚀 🐦 Twitter: [@chklamm](http://twitter.com/chklamm)
chompk/wav2vec2-large-xlsr-thai-tokenized
2021-04-16T14:04:53.000Z
[ "pytorch", "wav2vec2", "th", "dataset:common_voice", "transformers", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning", "license:apache-2.0" ]
automatic-speech-recognition
[ ".gitattributes", "README.md", "config.json", "optimizer.pt", "preprocessor_config.json", "pytorch_model.bin", "scheduler.pt", "trainer_state.json", "training_args.bin", "vocab.json" ]
chompk
60
transformers
--- language: th datasets: - common_voice tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning license: apache-2.0 --- # Wav2Vec2-Large-XLSR-53 in Thai Language (Train with deepcut tokenizer)
chrisevan/68
2021-04-13T15:06:32.000Z
[]
[ ".gitattributes", "4khdmovies", "GFHFH", "GodzillaVsKong", "InstantPlaySweepstakes", "README.md", "ksjvhkslvs", "sdfsfsfs", "sfsfsfsff", "skfjsfkslsdfbs", "ssfsfsf", "vdskfsdsfs", "zffsafsfsfsf" ]
chrisevan
0
chrisjay/fonxlsr
2021-03-26T13:01:10.000Z
[ "pytorch", "wav2vec2", "fon", "dataset:fon_dataset", "arxiv:2103.07762", "transformers", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "license:apache-2.0" ]
automatic-speech-recognition
[ ".gitattributes", "README.md", "config.json", "preprocessor_config.json", "pytorch_model.bin", "scheduler.pt", "special_tokens_map.json", "tokenizer_config.json", "trainer_state.json", "training_args.bin", "vocab.json", "test/test_fon_0.json", "test/test_fon_1.json", "test/test_fon_10.json", "test/test_fon_100.json", "test/test_fon_1000.json", "test/test_fon_1001.json", "test/test_fon_1002.json", "test/test_fon_1003.json", "test/test_fon_1004.json", "test/test_fon_1005.json", "test/test_fon_1006.json", "test/test_fon_1007.json", "test/test_fon_1008.json", "test/test_fon_1009.json", "test/test_fon_101.json", "test/test_fon_1010.json", "test/test_fon_1011.json", "test/test_fon_1012.json", "test/test_fon_1013.json", "test/test_fon_1014.json", "test/test_fon_1015.json", "test/test_fon_1016.json", "test/test_fon_1017.json", "test/test_fon_1018.json", "test/test_fon_1019.json", "test/test_fon_102.json", "test/test_fon_1020.json", "test/test_fon_1021.json", "test/test_fon_1022.json", "test/test_fon_1023.json", "test/test_fon_1024.json", "test/test_fon_1025.json", "test/test_fon_1026.json", "test/test_fon_1027.json", "test/test_fon_1028.json", "test/test_fon_1029.json", "test/test_fon_103.json", "test/test_fon_1030.json", "test/test_fon_1031.json", "test/test_fon_1032.json", "test/test_fon_1033.json", "test/test_fon_1034.json", "test/test_fon_1035.json", "test/test_fon_1036.json", "test/test_fon_1037.json", "test/test_fon_1038.json", "test/test_fon_1039.json", "test/test_fon_104.json", "test/test_fon_1040.json", "test/test_fon_1041.json", "test/test_fon_1042.json", "test/test_fon_1043.json", "test/test_fon_1044.json", "test/test_fon_1045.json", "test/test_fon_1046.json", "test/test_fon_1047.json", "test/test_fon_1048.json", "test/test_fon_1049.json", "test/test_fon_105.json", "test/test_fon_1050.json", "test/test_fon_1051.json", "test/test_fon_1052.json", "test/test_fon_1053.json", "test/test_fon_1054.json", "test/test_fon_1055.json", "test/test_fon_1056.json", 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"valid/valid_fon_562.json", "valid/valid_fon_563.json", "valid/valid_fon_564.json", "valid/valid_fon_565.json", "valid/valid_fon_566.json", "valid/valid_fon_567.json", "valid/valid_fon_568.json", "valid/valid_fon_569.json", "valid/valid_fon_57.json", "valid/valid_fon_570.json", "valid/valid_fon_571.json", "valid/valid_fon_572.json", "valid/valid_fon_573.json", "valid/valid_fon_574.json", "valid/valid_fon_575.json", "valid/valid_fon_576.json", "valid/valid_fon_577.json", "valid/valid_fon_578.json", "valid/valid_fon_579.json", "valid/valid_fon_58.json", "valid/valid_fon_580.json", "valid/valid_fon_581.json", "valid/valid_fon_582.json", "valid/valid_fon_583.json", "valid/valid_fon_584.json", "valid/valid_fon_585.json", "valid/valid_fon_586.json", "valid/valid_fon_587.json", "valid/valid_fon_588.json", "valid/valid_fon_589.json", "valid/valid_fon_59.json", "valid/valid_fon_590.json", "valid/valid_fon_591.json", "valid/valid_fon_592.json", "valid/valid_fon_593.json", "valid/valid_fon_594.json", "valid/valid_fon_595.json", "valid/valid_fon_596.json", "valid/valid_fon_597.json", "valid/valid_fon_598.json", "valid/valid_fon_599.json", "valid/valid_fon_6.json", "valid/valid_fon_60.json", "valid/valid_fon_600.json", "valid/valid_fon_601.json", "valid/valid_fon_602.json", "valid/valid_fon_603.json", "valid/valid_fon_604.json", "valid/valid_fon_605.json", "valid/valid_fon_606.json", "valid/valid_fon_607.json", "valid/valid_fon_608.json", "valid/valid_fon_609.json", "valid/valid_fon_61.json", "valid/valid_fon_610.json", "valid/valid_fon_611.json", "valid/valid_fon_612.json", "valid/valid_fon_613.json", "valid/valid_fon_614.json", "valid/valid_fon_615.json", "valid/valid_fon_616.json", "valid/valid_fon_617.json", "valid/valid_fon_618.json", "valid/valid_fon_619.json", "valid/valid_fon_62.json", "valid/valid_fon_620.json", "valid/valid_fon_621.json", "valid/valid_fon_622.json", "valid/valid_fon_623.json", "valid/valid_fon_624.json", "valid/valid_fon_625.json", "valid/valid_fon_626.json", "valid/valid_fon_627.json", "valid/valid_fon_628.json", "valid/valid_fon_629.json", "valid/valid_fon_63.json", "valid/valid_fon_630.json", "valid/valid_fon_631.json", "valid/valid_fon_632.json", "valid/valid_fon_633.json", "valid/valid_fon_634.json", "valid/valid_fon_635.json", "valid/valid_fon_636.json", "valid/valid_fon_637.json", "valid/valid_fon_638.json", "valid/valid_fon_639.json", "valid/valid_fon_64.json", "valid/valid_fon_640.json", "valid/valid_fon_641.json", "valid/valid_fon_642.json", "valid/valid_fon_643.json", "valid/valid_fon_644.json", "valid/valid_fon_645.json", "valid/valid_fon_646.json", "valid/valid_fon_647.json", "valid/valid_fon_648.json", "valid/valid_fon_649.json", "valid/valid_fon_65.json", "valid/valid_fon_650.json", "valid/valid_fon_651.json", "valid/valid_fon_652.json", "valid/valid_fon_653.json", "valid/valid_fon_654.json", "valid/valid_fon_655.json", "valid/valid_fon_656.json", "valid/valid_fon_657.json", "valid/valid_fon_658.json", "valid/valid_fon_659.json", "valid/valid_fon_66.json", "valid/valid_fon_660.json", "valid/valid_fon_661.json", "valid/valid_fon_662.json", "valid/valid_fon_663.json", "valid/valid_fon_664.json", "valid/valid_fon_665.json", "valid/valid_fon_666.json", "valid/valid_fon_667.json", "valid/valid_fon_668.json", "valid/valid_fon_669.json", "valid/valid_fon_67.json", "valid/valid_fon_670.json", "valid/valid_fon_671.json", "valid/valid_fon_672.json", "valid/valid_fon_673.json", "valid/valid_fon_674.json", "valid/valid_fon_675.json", "valid/valid_fon_676.json", "valid/valid_fon_677.json", "valid/valid_fon_678.json", "valid/valid_fon_679.json", "valid/valid_fon_68.json", "valid/valid_fon_680.json", "valid/valid_fon_681.json", "valid/valid_fon_682.json", "valid/valid_fon_683.json", "valid/valid_fon_684.json", "valid/valid_fon_685.json", "valid/valid_fon_686.json", "valid/valid_fon_687.json", "valid/valid_fon_688.json", "valid/valid_fon_689.json", "valid/valid_fon_69.json", "valid/valid_fon_690.json", "valid/valid_fon_691.json", "valid/valid_fon_692.json", "valid/valid_fon_693.json", "valid/valid_fon_694.json", "valid/valid_fon_695.json", "valid/valid_fon_696.json", "valid/valid_fon_697.json", "valid/valid_fon_698.json", "valid/valid_fon_699.json", "valid/valid_fon_7.json", "valid/valid_fon_70.json", "valid/valid_fon_700.json", "valid/valid_fon_701.json", "valid/valid_fon_702.json", "valid/valid_fon_703.json", "valid/valid_fon_704.json", "valid/valid_fon_705.json", "valid/valid_fon_706.json", "valid/valid_fon_707.json", "valid/valid_fon_708.json", "valid/valid_fon_709.json", "valid/valid_fon_71.json", "valid/valid_fon_710.json", "valid/valid_fon_711.json", "valid/valid_fon_712.json", "valid/valid_fon_713.json", "valid/valid_fon_714.json", "valid/valid_fon_715.json", "valid/valid_fon_716.json", "valid/valid_fon_717.json", "valid/valid_fon_718.json", "valid/valid_fon_719.json", "valid/valid_fon_72.json", "valid/valid_fon_720.json", "valid/valid_fon_721.json", "valid/valid_fon_722.json", "valid/valid_fon_723.json", "valid/valid_fon_724.json", "valid/valid_fon_725.json", "valid/valid_fon_726.json", "valid/valid_fon_727.json", "valid/valid_fon_728.json", "valid/valid_fon_729.json", "valid/valid_fon_73.json", "valid/valid_fon_730.json", "valid/valid_fon_731.json", "valid/valid_fon_732.json", "valid/valid_fon_733.json", "valid/valid_fon_734.json", "valid/valid_fon_735.json", "valid/valid_fon_736.json", "valid/valid_fon_737.json", "valid/valid_fon_738.json", "valid/valid_fon_739.json", "valid/valid_fon_74.json", "valid/valid_fon_740.json", "valid/valid_fon_741.json", "valid/valid_fon_742.json", "valid/valid_fon_743.json", "valid/valid_fon_744.json", "valid/valid_fon_745.json", "valid/valid_fon_746.json", "valid/valid_fon_747.json", "valid/valid_fon_748.json", "valid/valid_fon_749.json", "valid/valid_fon_75.json", "valid/valid_fon_750.json", "valid/valid_fon_751.json", "valid/valid_fon_752.json", "valid/valid_fon_753.json", "valid/valid_fon_754.json", "valid/valid_fon_755.json", "valid/valid_fon_756.json", "valid/valid_fon_757.json", "valid/valid_fon_758.json", "valid/valid_fon_759.json", "valid/valid_fon_76.json", "valid/valid_fon_760.json", "valid/valid_fon_761.json", "valid/valid_fon_762.json", "valid/valid_fon_763.json", "valid/valid_fon_764.json", "valid/valid_fon_765.json", "valid/valid_fon_766.json", "valid/valid_fon_767.json", "valid/valid_fon_768.json", "valid/valid_fon_769.json", "valid/valid_fon_77.json", "valid/valid_fon_770.json", "valid/valid_fon_771.json", "valid/valid_fon_772.json", "valid/valid_fon_773.json", "valid/valid_fon_774.json", "valid/valid_fon_775.json", "valid/valid_fon_776.json", "valid/valid_fon_777.json", "valid/valid_fon_778.json", "valid/valid_fon_779.json", "valid/valid_fon_78.json", "valid/valid_fon_780.json", "valid/valid_fon_781.json", "valid/valid_fon_782.json", "valid/valid_fon_783.json", "valid/valid_fon_784.json", "valid/valid_fon_785.json", "valid/valid_fon_786.json", "valid/valid_fon_787.json", "valid/valid_fon_788.json", "valid/valid_fon_789.json", "valid/valid_fon_79.json", "valid/valid_fon_790.json", "valid/valid_fon_791.json", "valid/valid_fon_792.json", "valid/valid_fon_793.json", "valid/valid_fon_794.json", "valid/valid_fon_795.json", "valid/valid_fon_796.json", "valid/valid_fon_797.json", "valid/valid_fon_798.json", "valid/valid_fon_799.json", "valid/valid_fon_8.json", "valid/valid_fon_80.json", "valid/valid_fon_800.json", "valid/valid_fon_801.json", "valid/valid_fon_802.json", "valid/valid_fon_803.json", "valid/valid_fon_804.json", "valid/valid_fon_805.json", "valid/valid_fon_806.json", "valid/valid_fon_807.json", "valid/valid_fon_808.json", "valid/valid_fon_809.json", "valid/valid_fon_81.json", "valid/valid_fon_810.json", "valid/valid_fon_811.json", "valid/valid_fon_812.json", "valid/valid_fon_813.json", "valid/valid_fon_814.json", "valid/valid_fon_815.json", "valid/valid_fon_816.json", "valid/valid_fon_817.json", "valid/valid_fon_818.json", "valid/valid_fon_819.json", "valid/valid_fon_82.json", "valid/valid_fon_820.json", "valid/valid_fon_821.json", "valid/valid_fon_822.json", "valid/valid_fon_823.json", "valid/valid_fon_824.json", "valid/valid_fon_825.json", "valid/valid_fon_826.json", "valid/valid_fon_827.json", "valid/valid_fon_828.json", "valid/valid_fon_829.json", "valid/valid_fon_83.json", "valid/valid_fon_830.json", "valid/valid_fon_831.json", "valid/valid_fon_832.json", "valid/valid_fon_833.json", "valid/valid_fon_834.json", "valid/valid_fon_835.json", "valid/valid_fon_836.json", "valid/valid_fon_837.json", "valid/valid_fon_838.json", "valid/valid_fon_839.json", "valid/valid_fon_84.json", "valid/valid_fon_840.json", "valid/valid_fon_841.json", "valid/valid_fon_842.json", "valid/valid_fon_843.json", "valid/valid_fon_844.json", "valid/valid_fon_845.json", "valid/valid_fon_846.json", "valid/valid_fon_847.json", "valid/valid_fon_848.json", "valid/valid_fon_849.json", "valid/valid_fon_85.json", "valid/valid_fon_850.json", "valid/valid_fon_851.json", "valid/valid_fon_852.json", "valid/valid_fon_853.json", "valid/valid_fon_854.json", "valid/valid_fon_855.json", "valid/valid_fon_856.json", "valid/valid_fon_857.json", "valid/valid_fon_858.json", "valid/valid_fon_859.json", "valid/valid_fon_86.json", "valid/valid_fon_860.json", "valid/valid_fon_861.json", "valid/valid_fon_862.json", "valid/valid_fon_863.json", "valid/valid_fon_864.json", "valid/valid_fon_865.json", "valid/valid_fon_866.json", "valid/valid_fon_867.json", "valid/valid_fon_868.json", "valid/valid_fon_869.json", "valid/valid_fon_87.json", "valid/valid_fon_870.json", "valid/valid_fon_871.json", "valid/valid_fon_872.json", "valid/valid_fon_873.json", "valid/valid_fon_874.json", "valid/valid_fon_875.json", "valid/valid_fon_876.json", "valid/valid_fon_877.json", "valid/valid_fon_878.json", "valid/valid_fon_879.json", "valid/valid_fon_88.json", "valid/valid_fon_880.json", "valid/valid_fon_881.json", "valid/valid_fon_882.json", "valid/valid_fon_883.json", "valid/valid_fon_884.json", "valid/valid_fon_885.json", "valid/valid_fon_886.json", "valid/valid_fon_887.json", "valid/valid_fon_888.json", "valid/valid_fon_889.json", "valid/valid_fon_89.json", "valid/valid_fon_890.json", "valid/valid_fon_891.json", "valid/valid_fon_892.json", "valid/valid_fon_893.json", "valid/valid_fon_894.json", "valid/valid_fon_895.json", "valid/valid_fon_896.json", "valid/valid_fon_897.json", "valid/valid_fon_898.json", "valid/valid_fon_899.json", "valid/valid_fon_9.json", "valid/valid_fon_90.json", "valid/valid_fon_900.json", "valid/valid_fon_901.json", "valid/valid_fon_902.json", "valid/valid_fon_903.json", "valid/valid_fon_904.json", "valid/valid_fon_905.json", "valid/valid_fon_906.json", "valid/valid_fon_907.json", "valid/valid_fon_908.json", "valid/valid_fon_909.json", "valid/valid_fon_91.json", "valid/valid_fon_910.json", "valid/valid_fon_911.json", "valid/valid_fon_912.json", "valid/valid_fon_913.json", "valid/valid_fon_914.json", "valid/valid_fon_915.json", "valid/valid_fon_916.json", "valid/valid_fon_917.json", "valid/valid_fon_918.json", "valid/valid_fon_919.json", "valid/valid_fon_92.json", "valid/valid_fon_920.json", "valid/valid_fon_921.json", "valid/valid_fon_922.json", "valid/valid_fon_923.json", "valid/valid_fon_924.json", "valid/valid_fon_925.json", "valid/valid_fon_926.json", "valid/valid_fon_927.json", "valid/valid_fon_928.json", "valid/valid_fon_929.json", "valid/valid_fon_93.json", "valid/valid_fon_930.json", "valid/valid_fon_931.json", "valid/valid_fon_932.json", "valid/valid_fon_933.json", "valid/valid_fon_934.json", "valid/valid_fon_935.json", "valid/valid_fon_936.json", "valid/valid_fon_937.json", "valid/valid_fon_938.json", "valid/valid_fon_939.json", "valid/valid_fon_94.json", "valid/valid_fon_940.json", "valid/valid_fon_941.json", "valid/valid_fon_942.json", "valid/valid_fon_943.json", "valid/valid_fon_944.json", "valid/valid_fon_945.json", "valid/valid_fon_946.json", "valid/valid_fon_947.json", "valid/valid_fon_948.json", "valid/valid_fon_949.json", "valid/valid_fon_95.json", "valid/valid_fon_950.json", "valid/valid_fon_951.json", "valid/valid_fon_952.json", "valid/valid_fon_953.json", "valid/valid_fon_954.json", "valid/valid_fon_955.json", "valid/valid_fon_956.json", "valid/valid_fon_957.json", "valid/valid_fon_958.json", "valid/valid_fon_959.json", "valid/valid_fon_96.json", "valid/valid_fon_960.json", "valid/valid_fon_961.json", "valid/valid_fon_962.json", "valid/valid_fon_963.json", "valid/valid_fon_964.json", "valid/valid_fon_965.json", "valid/valid_fon_966.json", "valid/valid_fon_967.json", "valid/valid_fon_968.json", "valid/valid_fon_969.json", "valid/valid_fon_97.json", "valid/valid_fon_970.json", "valid/valid_fon_971.json", "valid/valid_fon_972.json", "valid/valid_fon_973.json", "valid/valid_fon_974.json", "valid/valid_fon_975.json", "valid/valid_fon_976.json", "valid/valid_fon_977.json", "valid/valid_fon_978.json", "valid/valid_fon_979.json", "valid/valid_fon_98.json", "valid/valid_fon_980.json", "valid/valid_fon_981.json", "valid/valid_fon_982.json", "valid/valid_fon_983.json", "valid/valid_fon_984.json", "valid/valid_fon_985.json", "valid/valid_fon_986.json", "valid/valid_fon_987.json", "valid/valid_fon_988.json", "valid/valid_fon_989.json", "valid/valid_fon_99.json", "valid/valid_fon_990.json", "valid/valid_fon_991.json", "valid/valid_fon_992.json", "valid/valid_fon_993.json", "valid/valid_fon_994.json", "valid/valid_fon_995.json", "valid/valid_fon_996.json", "valid/valid_fon_997.json", "valid/valid_fon_998.json", "valid/valid_fon_999.json" ]
chrisjay
15
transformers
--- language: fon datasets: - fon_dataset metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: Fon XLSR Wav2Vec2 Large 53 results: - task: name: Speech Recognition type: automatic-speech-recognition dataset: name: fon type: fon_dataset args: fon metrics: - name: Test WER type: wer value: 14.97% --- # Wav2Vec2-Large-XLSR-53-Fon Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on [Fon (or Fongbe)](https://en.wikipedia.org/wiki/Fon_language) using the [Fon Dataset](https://github.com/laleye/pyFongbe/tree/master/data). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import json import random import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor #Load test_dataset from saved files in folder from datasets import load_dataset, load_metric #for test for root, dirs, files in os.walk(test/): test_dataset= load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train") #Remove unnecessary chars chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]' def remove_special_characters(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " return batch test_dataset = test_dataset.map(remove_special_characters) processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") #No need for resampling because audio dataset already at 16kHz #resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"]=speech_array.squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on our unique Fon test data. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re for root, dirs, files in os.walk(test/): test_dataset = load_dataset("json", data_files=[os.path.join(root,i) for i in files],split="train") chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“\\%\\‘\\”]' batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " " return batch test_dataset = test_dataset.map(remove_special_characters) wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") model = Wav2Vec2ForCTC.from_pretrained("chrisjay/wav2vec2-large-xlsr-53-fon") model.to("cuda") # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = speech_array[0].numpy() batch["sampling_rate"] = sampling_rate batch["target_text"] = batch["sentence"] return batch test_dataset = test_dataset.map(speech_file_to_array_fn) #Evaluation on test dataset def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 14.97 % ## Training The [Fon dataset](https://github.com/laleye/pyFongbe/tree/master/data) was split into `train`(8235 samples), `validation`(1107 samples), and `test`(1061 samples). The script used for training can be found [here](https://colab.research.google.com/drive/11l6qhJCYnPTG1TQZ8f3EvKB9z12TQi4g?usp=sharing) # Collaborators on this project - Chris C. Emezue ([Twitter](https://twitter.com/ChrisEmezue))|([email protected]) - Bonaventure F.P. Dossou (HuggingFace Username: [bonadossou](https://huggingface.co/bonadossou))|([Twitter](https://twitter.com/bonadossou))|([email protected]) ## This is a joint project continuing our research on [OkwuGbé: End-to-End Speech Recognition for Fon and Igbo](https://arxiv.org/abs/2103.07762)
chriskhanhtran/spanberta
2021-05-20T15:20:16.000Z
[ "pytorch", "jax", "roberta", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "config.json", "eval_results.txt", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "tokenizer_config.json", "vocab.json" ]
chriskhanhtran
275
transformers
chrisl/gpt-neo-2.7B
2021-04-02T21:14:08.000Z
[]
[ ".gitattributes" ]
chrisl
0
chrisliu298/arxiv_ai_gpt2
2021-05-21T14:59:11.000Z
[ "pytorch", "jax", "gpt2", "lm-head", "causal-lm", "en", "dataset:https://github.com/staeiou/arxiv_archive/tree/v1.0.1", "transformers", "arxiv", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "added_tokens.json", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
chrisliu298
114
transformers
--- language: "en" tags: - gpt2 - arxiv - transformers datasets: - https://github.com/staeiou/arxiv_archive/tree/v1.0.1 --- # ArXiv AI GPT-2 ## Model description This GPT-2 (774M) model is capable of generating abstracts given paper titles. It was trained using all research paper titles and abstracts under artificial intelligence (AI), machine learning (LG), computation and language (CL), and computer vision and pattern recognition (CV) on arXiv. ## Intended uses & limitations #### How to use To generate paper abstracts, use the provided `generate.py` [here](https://gist.github.com/chrisliu298/ccb8144888eace069da64ad3e6472d64). This is very similar to the HuggingFace's `run_generation.py` [here](https://github.com/huggingface/transformers/tree/master/examples/text-generation). You can simply replace the text with with your own model path (line 89) and change the input string to your paper title (line 127). If you want to use your own script, make sure to prepend `<|startoftext|> ` at the front and append ` <|sep|>` at the end of the paper title. ## Training data I selected a subset of the [arXiv Archive](https://github.com/staeiou/arxiv_archive) dataset (Geiger, 2019) as the training and evaluation data to fine-tune GPT-2. The original arXiv Archive dataset contains a full archive of metadata about papers on arxiv.org, from the start of the site in 1993 to the end of 2019. Our subset includes all the paper titles (query) and abstracts (context) under the Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Computation and Language (cs.CL), and Computer Vision and Pattern Recognition (cs.CV) categories. I provide the information of the sub-dataset and the distribution of the training and evaluation dataset as follows. | Splits | Count | Percentage (%) | BPE Token Count | | :--------: | :--------: | :------------: | :-------------: | | Train | 90,000 | 90.11 | 20,834,012 | | Validation | 4,940 | 4.95 | 1,195,056 | | Test | 4,940 | 4.95 | 1,218,754 | | **Total** | **99,880** | **100** | **23,247,822** | The original dataset is in the format of a tab-separated value, so we wrote a simple preprocessing script to convert it into a text file format, which is the input file type (a document) of the GPT-2 model. An example of a paper’s title and its abstract is shown below. ```text <|startoftext|> Some paper title <|sep|> Some paper abstract <|endoftext|> ``` Because there are a lot of cross-domain papers in the dataset, I deduplicate the dataset using the arXiv ID, which is unique for every paper. I sort the paper by submission date, by doing so, one can examine GPT-2’s ability to use learned terminologies when it is prompted with paper titles from the “future.” ## Training procedure I used block size = 512, batch size = 1, gradidnet accumulation = 1, learning rate = 1e-5, epochs = 5, and everything else follows the default model configuration. ## Eval results The resulting GPT-2 large model's perplexity score on the test set is **14.9413**. ## Reference ```bibtex @dataset{r_stuart_geiger_2019_2533436, author= {R. Stuart Geiger}, title={{ArXiV Archive: A tidy and complete archive of metadata for papers on arxiv.org, 1993-2019}}, month=jan, year= 2019, publisher={Zenodo}, version= {v1.0.1}, doi={10.5281/zenodo.2533436}, url={https://doi.org/10.5281/zenodo.2533436} } ```
christina/decoder-only-transformer-small
2021-04-17T20:49:16.000Z
[]
[ ".gitattributes", "small.pt" ]
christina
0
christina/decoder-only-transformer-x2small
2021-04-17T20:46:31.000Z
[]
[ ".gitattributes", "x2small.pt" ]
christina
0
christina/decoder-only-transformer-x3small
2021-04-17T20:48:49.000Z
[]
[ ".gitattributes", "x3small.pt" ]
christina
0
christina/decoder-only-transformer-x4small
2021-04-17T20:43:20.000Z
[]
[ ".gitattributes" ]
christina
0
christina/decoder-only-transformer-x5small
2021-04-17T20:49:33.000Z
[]
[ ".gitattributes", "x5small.pt" ]
christina
0
christina/decoder-only-transformer-x6small
2021-04-17T20:45:35.000Z
[]
[ ".gitattributes", "x6small.pt" ]
christina
0
christopherastone/distilgpt2-proofs
2021-05-21T15:01:54.000Z
[ "pytorch", "tf", "jax", "gpt2", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tf_model.h5", "tokenizer_config.json", "vocab.json" ]
christopherastone
14
transformers
[DistilGPT2](https://huggingface.co/distilgpt2) English language model fine-tuned on mathematical proofs extracted from [arXiv.org](https://arxiv.org) LaTeX sources from 1992 to 2020. Proofs have been cleaned up a bit. In particular, they use * `CITE` for any citation * `REF` for any reference * `MATH` for any LaTeX mathematical formula * `CASE:` for any `\\item` or labeled subcase. For text generation, I recommend prompts such as: * `Let MATH be given.` * `By the inductive hypothesis,` * `If MATH is a nonempty`
chu/KC.
2021-02-03T08:48:41.000Z
[]
[ ".gitattributes" ]
chu
0
cimm-kzn/endr-bert
2020-12-11T21:35:42.000Z
[ "pytorch", "ru", "en", "arxiv:2004.03659", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
cimm-kzn
322
transformers
--- language: - ru - en --- ## EnDR-BERT EnDR-BERT - Multilingual, Cased, which pretrained on the english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization and all the parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: https://yadi.sk/d/-PTn0xhk1PqvgQ ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. preprint: https://arxiv.org/abs/2004.03659 ``` @article{10.1093/bioinformatics/btaa675, author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey}, title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}", journal = {Bioinformatics}, year = {2020}, month = {07}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa675}, url = {https://doi.org/10.1093/bioinformatics/btaa675}, note = {btaa675}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf}, } ``` [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189. [link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf) ``` @article{tutubalina2017using, title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects}, author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE}, journal={Russian Chemical Bulletin}, volume={66}, number={11}, pages={2180--2189}, year={2017}, publisher={Springer} } ```
cimm-kzn/enrudr-bert
2020-12-11T21:35:46.000Z
[ "pytorch", "ru", "en", "arxiv:2004.03659", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
cimm-kzn
316
transformers
--- language: - ru - en --- ## EnRuDR-BERT EnRuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews) and english collection of consumer comments on drug administration from [2]. Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: https://yadi.sk/d/-PTn0xhk1PqvgQ ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews.//Bioinformatics. - 2020. preprint: https://arxiv.org/abs/2004.03659 ``` @article{10.1093/bioinformatics/btaa675, author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey}, title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}", journal = {Bioinformatics}, year = {2020}, month = {07}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa675}, url = {https://doi.org/10.1093/bioinformatics/btaa675}, note = {btaa675}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf}, } ``` [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects.//Russian Chemical Bulletin. – 2017. – Т. 66. – №. 11. – С. 2180-2189. [link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf) ``` @article{tutubalina2017using, title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects}, author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE}, journal={Russian Chemical Bulletin}, volume={66}, number={11}, pages={2180--2189}, year={2017}, publisher={Springer} } ```
cimm-kzn/rudr-bert
2020-12-14T14:50:15.000Z
[ "pytorch", "arxiv:2004.03659", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
cimm-kzn
42
transformers
## RuDR-BERT RuDR-BERT - Multilingual, Cased, which pretrained on the raw part of the RuDReC corpus (1.4M reviews). Pre-training was based on the [original BERT code](https://github.com/google-research/bert) provided by Google. In particular, Multi-BERT was for used for initialization; vocabulary of Russian subtokens and parameters are the same as in Multi-BERT. Training details are described in our paper. \ link: https://yadi.sk/d/-PTn0xhk1PqvgQ ## Citing & Authors If you find this repository helpful, feel free to cite our publication: [1] Tutubalina E, Alimova I, Miftahutdinov Z, et al. The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews. preprint: https://arxiv.org/abs/2004.03659 ``` @article{10.1093/bioinformatics/btaa675, author = {Tutubalina, Elena and Alimova, Ilseyar and Miftahutdinov, Zulfat and Sakhovskiy, Andrey and Malykh, Valentin and Nikolenko, Sergey}, title = "{The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews}", journal = {Bioinformatics}, year = {2020}, month = {07}, issn = {1367-4803}, doi = {10.1093/bioinformatics/btaa675}, url = {https://doi.org/10.1093/bioinformatics/btaa675}, note = {btaa675}, eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa675/33539752/btaa675.pdf}, } ``` [2] Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE Using semantic analysis of texts for the identification of drugs with similar therapeutic effects. [link to paper](https://www.researchgate.net/profile/Elena_Tutubalina/publication/323751823_Using_semantic_analysis_of_texts_for_the_identification_of_drugs_with_similar_therapeutic_effects/links/5bf7cfc3299bf1a0202cbc1f/Using-semantic-analysis-of-texts-for-the-identification-of-drugs-with-similar-therapeutic-effects.pdf) ``` @article{tutubalina2017using, title={Using semantic analysis of texts for the identification of drugs with similar therapeutic effects}, author={Tutubalina, EV and Miftahutdinov, Z Sh and Nugmanov, RI and Madzhidov, TI and Nikolenko, SI and Alimova, IS and Tropsha, AE}, journal={Russian Chemical Bulletin}, volume={66}, number={11}, pages={2180--2189}, year={2017}, publisher={Springer} } ```
ckiplab/albert-base-chinese-ner
2021-01-18T02:14:29.000Z
[ "pytorch", "albert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
408
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - albert - zh license: gpl-3.0 datasets: metrics: --- # CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/albert-base-chinese-pos
2021-01-18T02:14:29.000Z
[ "pytorch", "albert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
94
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - albert - zh license: gpl-3.0 datasets: metrics: --- # CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-pos') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/albert-base-chinese-ws
2021-01-18T02:14:29.000Z
[ "pytorch", "albert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
94
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - albert - zh license: gpl-3.0 datasets: metrics: --- # CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese-ws') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/albert-base-chinese
2021-01-18T02:14:29.000Z
[ "pytorch", "albert", "masked-lm", "zh", "transformers", "lm-head", "license:gpl-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
1,693
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - lm-head - albert - zh license: gpl-3.0 datasets: metrics: --- # CKIP ALBERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-base-chinese') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/albert-tiny-chinese-ner
2021-01-18T02:14:30.000Z
[ "pytorch", "albert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
279
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - albert - zh license: gpl-3.0 datasets: metrics: --- # CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/albert-tiny-chinese-pos
2021-01-18T02:14:30.000Z
[ "pytorch", "albert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
623
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - albert - zh license: gpl-3.0 datasets: metrics: --- # CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-pos') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/albert-tiny-chinese-ws
2021-01-18T02:14:30.000Z
[ "pytorch", "albert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
436
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - albert - zh license: gpl-3.0 datasets: metrics: --- # CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese-ws') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/albert-tiny-chinese
2021-01-18T02:14:30.000Z
[ "pytorch", "albert", "masked-lm", "zh", "transformers", "lm-head", "license:gpl-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
13,623
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - lm-head - albert - zh license: gpl-3.0 datasets: metrics: --- # CKIP ALBERT Tiny Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/bert-base-chinese-ner
2021-05-19T14:05:16.000Z
[ "pytorch", "jax", "bert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
6,551
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - bert - zh license: gpl-3.0 datasets: metrics: --- # CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ner') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/bert-base-chinese-pos
2021-05-19T14:06:30.000Z
[ "pytorch", "jax", "bert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
5,116
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - bert - zh license: gpl-3.0 datasets: metrics: --- # CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-pos') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/bert-base-chinese-ws
2021-05-19T14:07:21.000Z
[ "pytorch", "jax", "bert", "token-classification", "zh", "transformers", "license:gpl-3.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
6,917
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - token-classification - bert - zh license: gpl-3.0 datasets: metrics: --- # CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese-ws') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/bert-base-chinese
2021-05-19T14:08:23.000Z
[ "pytorch", "jax", "bert", "masked-lm", "zh", "transformers", "lm-head", "license:gpl-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
6,261
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - lm-head - bert - zh license: gpl-3.0 datasets: metrics: --- # CKIP BERT Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/bert-base-chinese') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
ckiplab/gpt2-base-chinese
2021-05-21T15:03:07.000Z
[ "pytorch", "jax", "gpt2", "lm-head", "causal-lm", "zh", "transformers", "license:gpl-3.0", "text-generation" ]
text-generation
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
ckiplab
1,715
transformers
--- language: - zh thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png tags: - pytorch - lm-head - gpt2 - zh license: gpl-3.0 datasets: metrics: --- # CKIP GPT2 Base Chinese This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). 這個專案提供了繁體中文的 transformers 模型(包含 ALBERT、BERT、GPT2)及自然語言處理工具(包含斷詞、詞性標記、實體辨識)。 ## Homepage * https://github.com/ckiplab/ckip-transformers ## Contributers * [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer) ## Usage Please use BertTokenizerFast as tokenizer instead of AutoTokenizer. 請使用 BertTokenizerFast 而非 AutoTokenizer。 ``` from transformers import ( BertTokenizerFast, AutoModel, ) tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') model = AutoModel.from_pretrained('ckiplab/gpt2-base-chinese') ``` For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers. 有關完整使用方法及其他資訊,請參見 https://github.com/ckiplab/ckip-transformers 。
cl-tohoku/bert-base-japanese-char-v2
2021-05-19T14:09:20.000Z
[ "pytorch", "tf", "jax", "bert", "masked-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "tf_model.h5", "tokenizer_config.json", "vocab.txt" ]
cl-tohoku
1,457
transformers
--- language: ja license: cc-by-sa-3.0 datasets: - wikipedia widget: - text: "東北大学で[MASK]の研究をしています。" --- # BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
cl-tohoku/bert-base-japanese-char-whole-word-masking
2021-05-19T14:11:32.000Z
[ "pytorch", "tf", "jax", "bert", "masked-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "tf_model.h5", "tokenizer_config.json", "vocab.txt" ]
cl-tohoku
3,197
transformers
--- language: ja license: cc-by-sa-3.0 datasets: - wikipedia widget: - text: "仙台は「[MASK]の都」と呼ばれている。" --- # BERT base Japanese (character tokenization, whole word masking enabled) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into characters. The vocabulary size is 4000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For the training of the MLM (masked language modeling) objective, we introduced the **Whole Word Masking** in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
cl-tohoku/bert-base-japanese-char
2021-05-19T14:12:19.000Z
[ "pytorch", "tf", "jax", "bert", "masked-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "tf_model.h5", "tokenizer_config.json", "vocab.txt" ]
cl-tohoku
6,776
transformers
--- language: ja license: cc-by-sa-3.0 datasets: - wikipedia widget: - text: "仙台は「[MASK]の都」と呼ばれている。" --- # BERT base Japanese (character tokenization) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by character-level tokenization. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into characters. The vocabulary size is 4000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
cl-tohoku/bert-base-japanese-v2
2021-05-19T14:13:14.000Z
[ "pytorch", "tf", "jax", "bert", "masked-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "tf_model.h5", "tokenizer_config.json", "vocab.txt" ]
cl-tohoku
30,910
transformers
--- language: ja license: cc-by-sa-3.0 datasets: - wikipedia widget: - text: "東北大学で[MASK]の研究をしています。" --- # BERT base Japanese (unidic-lite with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
cl-tohoku/bert-base-japanese-whole-word-masking
2021-05-19T14:14:14.000Z
[ "pytorch", "tf", "jax", "bert", "masked-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "tf_model.h5", "tokenizer_config.json", "vocab.txt" ]
cl-tohoku
103,741
transformers
--- language: ja license: cc-by-sa-3.0 datasets: - wikipedia widget: - text: "東北大学で[MASK]の研究をしています。" --- # BERT base Japanese (IPA dictionary, whole word masking enabled) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For the training of the MLM (masked language modeling) objective, we introduced the **Whole Word Masking** in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
cl-tohoku/bert-base-japanese
2021-05-19T14:15:43.000Z
[ "pytorch", "tf", "jax", "bert", "masked-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "tf_model.h5", "tokenizer_config.json", "vocab.txt" ]
cl-tohoku
22,661
transformers
--- language: ja license: cc-by-sa-3.0 datasets: - wikipedia widget: - text: "東北大学で[MASK]の研究をしています。" --- # BERT base Japanese (IPA dictionary) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the IPA dictionary, followed by the WordPiece subword tokenization. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v1.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The model is trained on Japanese Wikipedia as of September 1, 2019. To generate the training corpus, [WikiExtractor](https://github.com/attardi/wikiextractor) is used to extract plain texts from a dump file of Wikipedia articles. The text files used for the training are 2.6GB in size, consisting of approximately 17M sentences. ## Tokenization The texts are first tokenized by [MeCab](https://taku910.github.io/mecab/) morphological parser with the IPA dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32000. ## Training The model is trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments For training models, we used Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
cl-tohoku/bert-large-japanese-char
2021-05-19T14:17:40.000Z
[ "pytorch", "tf", "jax", "bert", "masked-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "tf_model.h5", "tokenizer_config.json", "vocab.txt" ]
cl-tohoku
778
transformers
--- language: ja license: cc-by-sa-3.0 datasets: - wikipedia widget: - text: "東北大学で[MASK]の研究をしています。" --- # BERT large Japanese (character-level tokenization with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
cl-tohoku/bert-large-japanese
2021-05-19T14:20:56.000Z
[ "pytorch", "tf", "jax", "bert", "masked-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "tf_model.h5", "tokenizer_config.json", "vocab.txt" ]
cl-tohoku
1,966
transformers
--- language: ja license: cc-by-sa-3.0 datasets: - wikipedia widget: - text: "東北大学で[MASK]の研究をしています。" --- # BERT large Japanese (unidic-lite with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm. The vocabulary size is 32768. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.
cla/sr
2021-04-03T09:04:57.000Z
[]
[ ".gitattributes" ]
cla
0
clagator/biobert_squad2_cased
2021-05-19T14:22:23.000Z
[ "pytorch", "jax", "bert", "question-answering", "transformers" ]
question-answering
[ ".gitattributes", "config.json", "flax_model.msgpack", "nbest_predictions_.json", "null_odds_.json", "predictions_.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.txt" ]
clagator
53
transformers
clagator/biobert_v1.1_pubmed_nli_sts
2021-05-19T14:23:22.000Z
[ "pytorch", "jax", "bert", "transformers" ]
[ ".gitattributes", "config.json", "flax_model.msgpack", "pytorch_model.bin", "sentence_bert_config.json", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
clagator
80
transformers
clarin-pl/long-former-polish
2021-04-26T14:00:04.000Z
[ "pytorch", "longformer", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "merges.txt", "pytorch_model.bin", "vocab.json" ]
clarin-pl
15
transformers
# Work in Progress Polish LongFormer The model has been trained for about 5% time of the target. We will publish new increments as they will be trained.
clarin-pl/roberta-polish-kgr10
2021-05-20T15:22:13.000Z
[ "pytorch", "jax", "roberta", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "merges.txt", "optimizer.pt", "pytorch_model.bin", "scheduler.pt", "special_tokens_map.json", "tokenizer_config.json", "trainer_state.json", "training_args.bin", "vocab.json" ]
clarin-pl
87
transformers
# Work in Progress Polish RoBERTa The model has been trained for about 5% time of the target. We will publish new increments as they will be trained. The model pre-trained on KGR10 corpora. More about model at [CLARIN-dspace](https://huggingface.co/clarin/roberta-polish-v1) ## Usage ## Huggingface model hub ## Acknowledgments [CLARIN-PL and CLARIN-BIZ project](https://clarin-pl.eu/)
classla/bcms-bertic-generator
2021-05-21T13:29:30.000Z
[ "pytorch", "electra", "pretraining", "hr", "bs", "sr", "cnr", "hbs", "transformers", "masked-lm", "license:apache-2.0", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "tokenizer_config.json", "vocab.txt" ]
classla
46
transformers
--- language: - hr - bs - sr - cnr - hbs tags: - masked-lm widget: - text: "Zovem se Marko i radim u [MASK]." license: apache-2.0 --- # BERTić&ast; [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian &ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This is the smaller generator of the main [discriminator model](https://huggingface.co/classla/bcms-bertic), useful if you want to continue pre-training the discriminator model. If you use the model, please cite the following paper: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ```
classla/bcms-bertic-geo
2021-02-20T06:46:06.000Z
[ "pytorch", "electra", "transformers" ]
[ ".gitattributes", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
classla
6
transformers
classla/bcms-bertic-ner
2021-05-03T14:21:01.000Z
[ "pytorch", "electra", "token-classification", "hr", "bs", "sr", "cnr", "hbs", "transformers", "license:apache-2.0" ]
token-classification
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
classla
31
transformers
--- language: - hr - bs - sr - cnr - hbs widget: - text: "Zovem se Marko i živim u Zagrebu. Studirao sam u Beogradu na Filozofskom fakultetu. Obožavam album Moanin." license: apache-2.0 --- # The [BERTić](https://huggingface.co/classla/bcms-bertic)&ast; [bert-ich] /bɜrtitʃ/ model fine-tuned for the task of named entity recognition in Bosnian, Croatian, Montenegrin and Serbian (BCMS) &ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This is a fine-tuned version of the [BERTić](https://huggingface.co/classla/bcms-bertic) model for the task of named entity recognition (PER, LOC, ORG, MISC). The fine-tuning was performed on the following datasets: - the [hr500k](http://hdl.handle.net/11356/1183) dataset, 500 thousand tokens in size, standard Croatian - the [SETimes.SR](http://hdl.handle.net/11356/1200) dataset, 87 thousand tokens in size, standard Serbian - the [ReLDI-hr](http://hdl.handle.net/11356/1241) dataset, 89 thousand tokens in size, Internet (Twitter) Croatian - the [ReLDI-sr](http://hdl.handle.net/11356/1240) dataset, 92 thousand tokens in size, Internet (Twitter) Serbian The data was augmented with missing diacritics and standard data was additionally over-represented. The F1 obtained on dev data (train and test was merged into train) is 91.38. For a more detailed per-dataset evaluation of the BERTić model on the NER task have a look at the [main model page](https://huggingface.co/classla/bcms-bertic). If you use this fine-tuned model, please cite the following paper: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ```
classla/bcms-bertic
2021-05-03T14:21:52.000Z
[ "pytorch", "electra", "pretraining", "hr", "bs", "sr", "cnr", "hbs", "transformers", "license:apache-2.0" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "tokenizer_config.json", "vocab.txt" ]
classla
421
transformers
--- language: - hr - bs - sr - cnr - hbs license: apache-2.0 --- # BERTić&ast; [bert-ich] /bɜrtitʃ/ - A transformer language model for Bosnian, Croatian, Montenegrin and Serbian &ast; The name should resemble the facts (1) that the model was trained in Zagreb, Croatia, where diminutives ending in -ić (as in fotić, smajlić, hengić etc.) are very popular, and (2) that most surnames in the countries where these languages are spoken end in -ić (with diminutive etymology as well). This Electra model was trained on more than 8 billion tokens of Bosnian, Croatian, Montenegrin and Serbian text. **&ast;new&ast;** We have published a version of this model fine-tuned on the named entity recognition task ([bcms-bertic-ner](https://huggingface.co/classla/bcms-bertic-ner)). If you use the model, please cite the following paper: ``` @inproceedings{ljubesic-lauc-2021-bertic, title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", month = apr, year = "2021", address = "Kiyv, Ukraine", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", pages = "37--42", } ``` ## Benchmarking Comparing this model to [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased) and [CroSloEngual BERT](https://huggingface.co/EMBEDDIA/crosloengual-bert) on the tasks of (1) part-of-speech tagging, (2) named entity recognition, (3) geolocation prediction, and (4) commonsense causal reasoning, shows the BERTić model to be superior to the other two. ### Part-of-speech tagging Evaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (&ast; p<=0.05, &ast;&ast; p<=0.01, &ast;&ast;&ast; p<=0.001, &ast;&ast;&ast;&ast;&ast; p<=0.0001). Dataset | Language | Variety | CLASSLA | mBERT | cseBERT | BERTić ---|---|---|---|---|---|--- hr500k | Croatian | standard | 93.87 | 94.60 | 95.74 | **95.81&ast;&ast;&ast;** reldi-hr | Croatian | internet non-standard | - | 88.87 | 91.63 | **92.28&ast;&ast;&ast;** SETimes.SR | Serbian | standard | 95.00 | 95.50 | **96.41** | 96.31 reldi-sr | Serbian | internet non-standard | - | 91.26 | 93.54 | **93.90&ast;&ast;&ast;** ### Named entity recognition Evaluation metric is (seqeval) microF1. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (&ast; p<=0.05, &ast;&ast; p<=0.01, &ast;&ast;&ast; p<=0.001, &ast;&ast;&ast;&ast;&ast; p<=0.0001). Dataset | Language | Variety | CLASSLA | mBERT | cseBERT | BERTić ---|---|---|---|---|---|--- hr500k | Croatian | standard | 80.13 | 85.67 | 88.98 | **89.21&ast;&ast;&ast;&ast;** reldi-hr | Croatian | internet non-standard | - | 76.06 | 81.38 | **83.05&ast;&ast;&ast;&ast;** SETimes.SR | Serbian | standard | 84.64 | **92.41** | 92.28 | 92.02 reldi-sr | Serbian | internet non-standard | - | 81.29 | 82.76 | **87.92&ast;&ast;&ast;&ast;** ### Geolocation prediction The dataset comes from the VarDial 2020 evaluation campaign's shared task on [Social Media variety Geolocation prediction](https://sites.google.com/view/vardial2020/evaluation-campaign). The task is to predict the latitude and longitude of a tweet given its text. Evaluation metrics are median and mean of distance between gold and predicted geolocations (lower is better). No statistical significance is computed due to large test set (39,723 instances). Centroid baseline predicts each text to be created in the centroid of the training dataset. System | Median | Mean ---|---|--- centroid | 107.10 | 145.72 mBERT | 42.25 | 82.05 cseBERT | 40.76 | 81.88 BERTić | **37.96** | **79.30** ### Choice Of Plausible Alternatives The dataset is a translation of the [COPA dataset](https://people.ict.usc.edu/~gordon/copa.html) into Croatian ([link to the dataset](http://hdl.handle.net/11356/1404)). Evaluation metric is accuracy. Reported are means of five runs. Best results are presented in bold. Statistical significance is calculated between two best-performing systems via a two-tailed t-test (&ast; p<=0.05, &ast;&ast; p<=0.01, &ast;&ast;&ast; p<=0.001, &ast;&ast;&ast;&ast;&ast; p<=0.0001). System | Accuracy ---|--- random | 50.00 mBERT | 54.12 cseBERT | 61.80 BERTić | **65.76&ast;&ast;**
classla/bert-base-german-dbmdz-uncased-geo
2021-05-19T14:23:58.000Z
[ "pytorch", "bert", "transformers" ]
[ ".gitattributes", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
classla
9
transformers
classla/swissbert-geo
2021-05-19T14:24:21.000Z
[ "pytorch", "bert", "transformers" ]
[ ".gitattributes", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
classla
8
transformers
claudelkros/T5_french_wiki_summarizer
2021-05-29T22:01:39.000Z
[ "pytorch", "t5", "seq2seq", "transformers", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "tokenizer.json" ]
claudelkros
32
transformers
claudelkros/bert-base-french
2020-09-15T00:05:37.000Z
[ "pytorch", "transformers" ]
[ ".gitattributes", "config.json", "pytorch_model.bin", "vocab.txt" ]
claudelkros
21
transformers
clem/bert_portuguese
2020-12-18T19:35:08.000Z
[]
[ ".gitattributes" ]
clem
0
cling371/modeling_test
2021-06-11T07:43:33.000Z
[ "pytorch", "distilbert", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "config.json", "pytorch_model.bin" ]
cling371
0
transformers
clue/albert_chinese_small
2020-12-11T21:35:52.000Z
[ "pytorch", "albert", "zh", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "vocab.txt" ]
clue
597
transformers
--- language: zh --- ## albert_chinese_small ### Overview **Language model:** albert-small **Model size:** 18.5M **Language:** Chinese **Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020) **Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE) ### Results For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE). ### Usage **NOTE:**Since sentencepiece is not used in `albert_chinese_small` model, you have to call **BertTokenizer** instead of AlbertTokenizer !!! ``` import torch from transformers import BertTokenizer, AlbertModel tokenizer = BertTokenizer.from_pretrained("clue/albert_chinese_small") albert = AlbertModel.from_pretrained("clue/albert_chinese_small") ``` ### About CLUE benchmark Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard. Github: https://github.com/CLUEbenchmark Website: https://www.cluebenchmarks.com/
clue/albert_chinese_tiny
2020-12-11T21:35:55.000Z
[ "pytorch", "albert", "zh", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "vocab.txt" ]
clue
912
transformers
--- language: zh --- ## albert_chinese_tiny ### Overview **Language model:** albert-tiny **Model size:** 16M **Language:** Chinese **Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020) **Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE) ### Results For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE). ### Usage **NOTE:**Since sentencepiece is not used in `albert_chinese_tiny` model, you have to call **BertTokenizer** instead of AlbertTokenizer !!! ``` import torch from transformers import BertTokenizer, AlbertModel tokenizer = BertTokenizer.from_pretrained("clue/albert_chinese_tiny") albert = AlbertModel.from_pretrained("clue/albert_chinese_tiny") ``` ### About CLUE benchmark Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard. Github: https://github.com/CLUEbenchmark Website: https://www.cluebenchmarks.com/
clue/roberta_chinese_3L312_clue_tiny
2021-05-20T15:22:48.000Z
[ "pytorch", "jax", "roberta", "zh", "arxiv:2003.01355", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "vocab.txt" ]
clue
104
transformers
--- language: zh --- # Introduction This model was trained on TPU and the details are as follows: ## Model ## | Model_name | params | size | Training_corpus | Vocab | | :------------------------------------------ | :----- | :------- | :----------------- | :-----------: | | **`RoBERTa-tiny-clue`** <br/>Super_small_model | 7.5M | 28.3M | **CLUECorpus2020** | **CLUEVocab** | | **`RoBERTa-tiny-pair`** <br/>Super_small_sentence_pair_model | 7.5M | 28.3M | **CLUECorpus2020** | **CLUEVocab** | | **`RoBERTa-tiny3L768-clue`** <br/>small_model | 38M | 110M | **CLUECorpus2020** | **CLUEVocab** | | **`RoBERTa-tiny3L312-clue`** <br/>small_model | <7.5M | 24M | **CLUECorpus2020** | **CLUEVocab** | | **`RoBERTa-large-clue`** <br/> Large_model | 290M | 1.20G | **CLUECorpus2020** | **CLUEVocab** | | **`RoBERTa-large-pair`** <br/>Large_sentence_pair_model | 290M | 1.20G | **CLUECorpus2020** | **CLUEVocab** | ### Usage With the help of[Huggingface-Transformers 2.5.1](https://github.com/huggingface/transformers), you could use these model as follows ``` tokenizer = BertTokenizer.from_pretrained("MODEL_NAME") model = BertModel.from_pretrained("MODEL_NAME") ``` `MODEL_NAME`: | Model_NAME | MODEL_LINK | | -------------------------- | ------------------------------------------------------------ | | **RoBERTa-tiny-clue** | [`clue/roberta_chinese_clue_tiny`](https://huggingface.co/clue/roberta_chinese_clue_tiny) | | **RoBERTa-tiny-pair** | [`clue/roberta_chinese_pair_tiny`](https://huggingface.co/clue/roberta_chinese_pair_tiny) | | **RoBERTa-tiny3L768-clue** | [`clue/roberta_chinese_3L768_clue_tiny`](https://huggingface.co/clue/roberta_chinese_3L768_clue_tiny) | | **RoBERTa-tiny3L312-clue** | [`clue/roberta_chinese_3L312_clue_tiny`](https://huggingface.co/clue/roberta_chinese_3L312_clue_tiny) | | **RoBERTa-large-clue** | [`clue/roberta_chinese_clue_large`](https://huggingface.co/clue/roberta_chinese_clue_large) | | **RoBERTa-large-pair** | [`clue/roberta_chinese_pair_large`](https://huggingface.co/clue/roberta_chinese_pair_large) | ## Details Please read <a href='https://arxiv.org/pdf/2003.01355'>https://arxiv.org/pdf/2003.01355. Please visit our repository: https://github.com/CLUEbenchmark/CLUEPretrainedModels.git
clue/roberta_chinese_3L768_clue_tiny
2021-05-20T15:23:12.000Z
[ "pytorch", "jax", "roberta", "transformers" ]
[ ".gitattributes", "config.json", "flax_model.msgpack", "pytorch_model.bin", "vocab.txt" ]
clue
307
transformers
clue/roberta_chinese_base
2021-05-20T15:23:58.000Z
[ "pytorch", "jax", "roberta", "zh", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "vocab.txt" ]
clue
182
transformers
--- language: zh --- ## roberta_chinese_base ### Overview **Language model:** roberta-base **Model size:** 392M **Language:** Chinese **Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020) **Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE) ### Results For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE). ### Usage **NOTE:** You have to call **BertTokenizer** instead of RobertaTokenizer !!! ``` import torch from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained("clue/roberta_chinese_base") roberta = BertModel.from_pretrained("clue/roberta_chinese_base") ``` ### About CLUE benchmark Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard. Github: https://github.com/CLUEbenchmark Website: https://www.cluebenchmarks.com/
clue/roberta_chinese_clue_large
2021-05-20T15:26:30.000Z
[ "pytorch", "jax", "roberta", "transformers" ]
[ ".gitattributes", "config.json", "flax_model.msgpack", "pytorch_model.bin", "vocab.txt" ]
clue
109
transformers
clue/roberta_chinese_clue_tiny
2021-05-20T15:27:44.000Z
[ "pytorch", "jax", "roberta", "transformers" ]
[ ".gitattributes", "config.json", "flax_model.msgpack", "pytorch_model.bin", "vocab.txt" ]
clue
231
transformers
clue/roberta_chinese_large
2021-05-20T15:28:53.000Z
[ "pytorch", "jax", "roberta", "zh", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "vocab.txt" ]
clue
92
transformers
--- language: zh --- ## roberta_chinese_large ### Overview **Language model:** roberta-large **Model size:** 1.2G **Language:** Chinese **Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020) **Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE) ### Results For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE). ### Usage **NOTE:** You have to call **BertTokenizer** instead of RobertaTokenizer !!! ``` import torch from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained("clue/roberta_chinese_large") roberta = BertModel.from_pretrained("clue/roberta_chinese_large") ``` ### About CLUE benchmark Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard. Github: https://github.com/CLUEbenchmark Website: https://www.cluebenchmarks.com/
clue/roberta_chinese_pair_large
2021-05-20T15:31:42.000Z
[ "pytorch", "jax", "roberta", "transformers" ]
[ ".gitattributes", "config.json", "flax_model.msgpack", "pytorch_model.bin", "vocab.txt" ]
clue
37
transformers
clue/roberta_chinese_pair_tiny
2021-05-20T15:33:09.000Z
[ "pytorch", "jax", "roberta", "transformers" ]
[ ".gitattributes", "config.json", "flax_model.msgpack", "pytorch_model.bin", "vocab.txt" ]
clue
126
transformers
clue/xlnet_chinese_large
2020-12-11T21:36:08.000Z
[ "pytorch", "xlnet", "zh", "transformers" ]
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "spiece.model" ]
clue
85
transformers
--- language: zh --- ## xlnet_chinese_large ### Overview **Language model:** xlnet-large **Model size:** 1.3G **Language:** Chinese **Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020) **Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE) ### Results For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE). ### Usage ``` import torch from transformers import XLNetTokenizer,XLNetModel tokenizer = XLNetTokenizer.from_pretrained("clue/xlnet_chinese_large") xlnet = XLNetModel.from_pretrained("clue/xlnet_chinese_large") ``` ### About CLUE benchmark Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard. Github: https://github.com/CLUEbenchmark Website: https://www.cluebenchmarks.com/
clulab/roberta-timex-semeval
2021-05-20T15:34:00.000Z
[ "pytorch", "jax", "roberta", "token-classification", "transformers" ]
token-classification
[ ".gitattributes", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
clulab
77
transformers
cnrcastroli/njhyj
2021-03-04T21:52:35.000Z
[]
[ ".gitattributes" ]
cnrcastroli
0
codegram/calbert-base-uncased
2020-12-11T21:36:11.000Z
[ "pytorch", "albert", "ca", "transformers", "masked-lm", "catalan", "exbert", "license:mit", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "vocab.json" ]
codegram
81
transformers
--- language: "ca" tags: - masked-lm - catalan - exbert license: mit --- # Calbert: a Catalan Language Model ## Introduction CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture. It is now available on Hugging Face in its `tiny-uncased` version and `base-uncased` (the one you're looking at) as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/). For further information or requests, please go to the [GitHub repository](https://github.com/codegram/calbert) ## Pre-trained models | Model | Arch. | Training data | | ----------------------------------- | -------------- | ---------------------- | | `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) | | `codegram` / `calbert-base-uncased` | Base (uncased) | OSCAR (4.3 GB of text) | ## How to use Calbert with HuggingFace #### Load Calbert and its tokenizer: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-base-uncased") model = AutoModel.from_pretrained("codegram/calbert-base-uncased") model.eval() # disable dropout (or leave in train mode to finetune ``` #### Filling masks using pipeline ```python from transformers import pipeline calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-base-uncased", tokenizer="codegram/calbert-base-uncased") results = calbert_fill_mask("M'agrada [MASK] això") # results # [{'sequence': "[CLS] m'agrada molt aixo[SEP]", 'score': 0.614592969417572, 'token': 61}, # {'sequence': "[CLS] m'agrada moltíssim aixo[SEP]", 'score': 0.06058056280016899, 'token': 4867}, # {'sequence': "[CLS] m'agrada més aixo[SEP]", 'score': 0.017195818945765495, 'token': 43}, # {'sequence': "[CLS] m'agrada llegir aixo[SEP]", 'score': 0.016321714967489243, 'token': 684}, # {'sequence': "[CLS] m'agrada escriure aixo[SEP]", 'score': 0.012185849249362946, 'token': 1306}] ``` #### Extract contextual embedding features from Calbert output ```python import torch # Tokenize in sub-words with SentencePiece tokenized_sentence = tokenizer.tokenize("M'és una mica igual") # ['▁m', "'", 'es', '▁una', '▁mica', '▁igual'] # 1-hot encode and add special starting and end tokens encoded_sentence = tokenizer.encode(tokenized_sentence) # [2, 109, 7, 71, 36, 371, 1103, 3] # NB: Can be done in one step : tokenize.encode("M'és una mica igual") # Feed tokens to Calbert as a torch tensor (batch dim 1) encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0) embeddings, _ = model(encoded_sentence) embeddings.size() # torch.Size([1, 8, 768]) embeddings.detach() # tensor([[[-0.0261, 0.1166, -0.1075, ..., -0.0368, 0.0193, 0.0017], # [ 0.1289, -0.2252, 0.9881, ..., -0.1353, 0.3534, 0.0734], # [-0.0328, -1.2364, 0.9466, ..., 0.3455, 0.7010, -0.2085], # ..., # [ 0.0397, -1.0228, -0.2239, ..., 0.2932, 0.1248, 0.0813], # [-0.0261, 0.1165, -0.1074, ..., -0.0368, 0.0193, 0.0017], # [-0.1934, -0.2357, -0.2554, ..., 0.1831, 0.6085, 0.1421]]]) ``` ## Authors CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research. <a href="https://huggingface.co/exbert/?model=codegram/calbert-base-uncased&modelKind=bidirectional&sentence=M%27agradaria%20força%20saber-ne%20més"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
codegram/calbert-tiny-uncased
2020-12-11T21:36:14.000Z
[ "pytorch", "albert", "ca", "transformers", "masked-lm", "catalan", "exbert", "license:mit", "fill-mask" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json", "vocab.json" ]
codegram
26
transformers
--- language: "ca" tags: - masked-lm - catalan - exbert license: mit --- # Calbert: a Catalan Language Model ## Introduction CALBERT is an open-source language model for Catalan pretrained on the ALBERT architecture. It is now available on Hugging Face in its `tiny-uncased` version (the one you're looking at) and `base-uncased` as well, and was pretrained on the [OSCAR dataset](https://traces1.inria.fr/oscar/). For further information or requests, please go to the [GitHub repository](https://github.com/codegram/calbert) ## Pre-trained models | Model | Arch. | Training data | | ----------------------------------- | -------------- | ---------------------- | | `codegram` / `calbert-tiny-uncased` | Tiny (uncased) | OSCAR (4.3 GB of text) | | `codegram` / `calbert-base-uncased` | Base (uncased) | OSCAR (4.3 GB of text) | ## How to use Calbert with HuggingFace #### Load Calbert and its tokenizer: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("codegram/calbert-tiny-uncased") model = AutoModel.from_pretrained("codegram/calbert-tiny-uncased") model.eval() # disable dropout (or leave in train mode to finetune ``` #### Filling masks using pipeline ```python from transformers import pipeline calbert_fill_mask = pipeline("fill-mask", model="codegram/calbert-tiny-uncased", tokenizer="codegram/calbert-tiny-uncased") results = calbert_fill_mask("M'agrada [MASK] això") # results # [{'sequence': "[CLS] m'agrada molt aixo[SEP]", 'score': 0.4403671622276306, 'token': 61}, # {'sequence': "[CLS] m'agrada més aixo[SEP]", 'score': 0.050061386078596115, 'token': 43}, # {'sequence': "[CLS] m'agrada veure aixo[SEP]", 'score': 0.026286985725164413, 'token': 157}, # {'sequence': "[CLS] m'agrada bastant aixo[SEP]", 'score': 0.022483550012111664, 'token': 2143}, # {'sequence': "[CLS] m'agrada moltíssim aixo[SEP]", 'score': 0.014491282403469086, 'token': 4867}] ``` #### Extract contextual embedding features from Calbert output ```python import torch # Tokenize in sub-words with SentencePiece tokenized_sentence = tokenizer.tokenize("M'és una mica igual") # ['▁m', "'", 'es', '▁una', '▁mica', '▁igual'] # 1-hot encode and add special starting and end tokens encoded_sentence = tokenizer.encode(tokenized_sentence) # [2, 109, 7, 71, 36, 371, 1103, 3] # NB: Can be done in one step : tokenize.encode("M'és una mica igual") # Feed tokens to Calbert as a torch tensor (batch dim 1) encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0) embeddings, _ = model(encoded_sentence) embeddings.size() # torch.Size([1, 8, 312]) embeddings.detach() # tensor([[[-0.2726, -0.9855, 0.9643, ..., 0.3511, 0.3499, -0.1984], # [-0.2824, -1.1693, -0.2365, ..., -3.1866, -0.9386, -1.3718], # [-2.3645, -2.2477, -1.6985, ..., -1.4606, -2.7294, 0.2495], # ..., # [ 0.8800, -0.0244, -3.0446, ..., 0.5148, -3.0903, 1.1879], # [ 1.1300, 0.2425, 0.2162, ..., -0.5722, -2.2004, 0.4045], # [ 0.4549, -0.2378, -0.2290, ..., -2.1247, -2.2769, -0.0820]]]) ``` ## Authors CALBERT was trained and evaluated by [Txus Bach](https://twitter.com/txustice), as part of [Codegram](https://www.codegram.com)'s applied research. <a href="https://huggingface.co/exbert/?model=codegram/calbert-tiny-uncased&modelKind=bidirectional&sentence=M%27agradaria%20força%20saber-ne%20més"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>
codevan/CDLM
2021-02-22T17:08:50.000Z
[ "pytorch", "longformer", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "added_tokens.json", "config.json", "merges.txt", "optimizer.pt", "pytorch_model.bin", "scheduler.pt", "special_tokens_map.json", "tokenizer_config.json", "trainer_state.json", "training_args.bin", "vocab.json" ]
codevan
7
transformers
codistai/codeBERT-small-v2
2021-05-20T15:35:42.000Z
[ "pytorch", "jax", "roberta", "masked-lm", "transformers", "fill-mask" ]
fill-mask
[ ".gitattributes", "config.json", "eval_results_lm.txt", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "training_args.bin", "vocab.json" ]
codistai
30
transformers
cointegrated/LaBSE-en-ru
2021-06-09T22:30:35.000Z
[ "pytorch", "bert", "pretraining", "ru", "en", "arxiv:2007.01852", "transformers", "feature-extraction", "embeddings" ]
feature-extraction
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.txt" ]
cointegrated
137
transformers
--- language: ["ru", "en"] tags: - feature-extraction - embeddings --- # LaBSE for English and Russian This is a truncated version of [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE), which is, in turn, a port of [LaBSE](https://tfhub.dev/google/LaBSE/1) by Google. The current model has only English and Russian tokens left in the vocabulary. Thus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings. To get the sentence embeddings, you can use the following code: ```python import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cointegrated/LaBSE-en-ru") model = AutoModel.from_pretrained("cointegrated/LaBSE-en-ru") sentences = ["Hello World", "Привет Мир"] encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=64, return_tensors='pt') with torch.no_grad(): model_output = model(**encoded_input) embeddings = model_output.pooler_output embeddings = torch.nn.functional.normalize(embeddings) print(embeddings) ``` ## Reference: Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Narveen Ari, Wei Wang. [Language-agnostic BERT Sentence Embedding](https://arxiv.org/abs/2007.01852). July 2020 License: [https://tfhub.dev/google/LaBSE/1](https://tfhub.dev/google/LaBSE/1)
cointegrated/rubert-tiny
2021-06-15T06:03:38.000Z
[ "pytorch", "bert", "pretraining", "ru", "en", "transformers", "russian", "fill-mask", "embeddings", "masked-lm", "tiny", "license:mit" ]
fill-mask
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", "vocab.txt" ]
cointegrated
1,486
transformers
--- language: ["ru", "en"] tags: - russian - fill-mask - pretraining - embeddings - masked-lm - tiny license: mit widget: - text: "Миниатюрная модель для [MASK] разных задач." --- This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model for Russian and English (45 MB, 12M parameters). This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 smaller and faster than a base-sized BERT. Its `[CLS]` embeddings can be used as a sentence representation aligned between Russian and English. It was trained on the [Yandex Translate corpus](https://translate.yandex.ru/corpus), [OPUS-100](https://huggingface.co/datasets/opus100) and [Tatoeba](https://huggingface.co/datasets/tatoeba), using MLM loss (distilled from [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)), translation ranking loss, and `[CLS]` embeddings distilled from [LaBSE](https://huggingface.co/sentence-transformers/LaBSE), [rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence), Laser and USE. There is a more detailed [description in Russian](https://habr.com/ru/post/562064/). Sentence embeddings can be produced as follows: ```python # pip install transformers sentencepiece import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny") model = AutoModel.from_pretrained("cointegrated/rubert-tiny") # model.cuda() # uncomment it if you have a GPU def embed_bert_cls(text, model, tokenizer): t = tokenizer(text, padding=True, truncation=True, return_tensors='pt') with torch.no_grad(): model_output = model(**{k: v.to(model.device) for k, v in t.items()}) embeddings = model_output.last_hidden_state[:, 0, :] embeddings = torch.nn.functional.normalize(embeddings) return embeddings[0].cpu().numpy() print(embed_bert_cls('привет мир', model, tokenizer).shape) # (312,) ```
cointegrated/rut5-base-multitask
2021-05-13T23:51:45.000Z
[ "pytorch", "t5", "seq2seq", "ru", "en", "transformers", "russian", "license:mit", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json" ]
cointegrated
131
transformers
--- language: ["ru", "en"] tags: - russian license: mit widget: - text: "fill | Почему они не ___ на меня?" --- This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) with only some Rusian and English embeddings left. The model has been fine-tuned for several tasks with sentences or short paragraphs: * translation (`translate ru-en` and `translate en-ru`) * Paraphrasing (`paraphrase`) * Filling gaps in a text (`fill`). The gaps can be denoted as `___` or `_3_`, where `3` is the approximate number of words that should be inserted. * Restoring the text from a noisy bag of words (`assemble`) * Simplification of texts (`simplify`) * Dialogue response generation (`reply` based on fiction and `answer` based on online forums) * Open-book question answering (`comprehend`) * Asking questions about a text (`ask`) * News title generation (`headline`) For each task, the task name is joined with the input text by the ` | ` separator. The model can be run with the following code: ``` # !pip install transformers sentencepiece import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-base-multitask") model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-base-multitask") def generate(text, **kwargs): inputs = tokenizer(text, return_tensors='pt') with torch.no_grad(): hypotheses = model.generate(**inputs, num_beams=5, **kwargs) return tokenizer.decode(hypotheses[0], skip_special_tokens=True) ``` The model can be applied to each of the pretraining tasks: ``` print(generate('translate ru-en | Каждый охотник желает знать, где сидит фазан.')) # Each hunter wants to know, where he is. print(generate('paraphrase | Каждый охотник желает знать, где сидит фазан.', encoder_no_repeat_ngram_size=1, repetition_penalty=0.5, no_repeat_ngram_size=1)) # В любом случае каждый рыбак мечтает познакомиться со своей фермой print(generate('fill | Каждый охотник _3_, где сидит фазан.')) # смотрит на озеро print(generate('assemble | охотник каждый знать фазан сидит')) # Каждый охотник знает, что фазан сидит. print(generate('simplify | Местным продуктом-специалитетом с защищённым географическим наименованием по происхождению считается люнебургский степной барашек.', max_length=32)) # Местным продуктом-специалитетом считается люнебургский степной барашек. print(generate('reply | Помогите мне закадрить девушку')) # Что я хочу? print(generate('answer | Помогите мне закадрить девушку')) # я хочу познакомиться с девушкой!!!!!!!! print(generate("comprehend | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, " "прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо. Вопрос: откуда приехал Морган?")) # из Австралии print(generate("ask | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, " "прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо.", max_length=32)) # Что разворачивается на фоне земельного конфликта между владельцами овец и ранчеро? print(generate("headline | На фоне земельного конфликта между владельцами овец и ранчеро разворачивается история любви овцевода Моргана Лейна, " "прибывшего в США из Австралии, и Марии Синглетон, владелицы богатого скотоводческого ранчо.", max_length=32)) # На фоне земельного конфликта разворачивается история любви овцевода Моргана Лейна и Марии Синглетон ``` However, it is strongly recommended that you fine tune the model for your own task.
cointegrated/rut5-base
2021-05-04T20:30:24.000Z
[ "pytorch", "t5", "seq2seq", "ru", "en", "transformers", "russian", "license:mit", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json" ]
cointegrated
342
transformers
--- language: ["ru", "en"] tags: - russian license: mit --- This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Russian and some English embeddings left. * The original model has 582M parameters, with 384M of them being input and output embeddings. * After shrinking the `sentencepiece` vocabulary from 250K to 30K (top 10K English and top 20K Russian tokens) the number of model parameters reduced to 244M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one. The creation of this model is described in the post [How to adapt a multilingual T5 model for a single language](https://cointegrated.medium.com/how-to-adapt-a-multilingual-t5-model-for-a-single-language-b9f94f3d9c90) along with the source code.
cointegrated/rut5-small-normalizer
2021-04-28T12:32:40.000Z
[ "pytorch", "t5", "seq2seq", "ru", "transformers", "normalization", "denoising autoencoder", "russian", "license:mit", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json" ]
cointegrated
296
transformers
--- language: "ru" tags: - normalization - denoising autoencoder - russian widget: - text: "меня тобой не понимать" license: mit --- This is a small Russian denoising autoencoder. It can be used for restoring corrupted sentences. This model was produced by fine-tuning the [rut5-small](https://huggingface.co/cointegrated/rut5-small) model on the task of reconstructing a sentence: * restoring word positions (after slightly shuffling them) * restoring dropped words and punctuation marks (after dropping some of them randomly) * restoring inflection of words (after changing their inflection randomly using [natasha](https://github.com/natasha/natasha) and [pymorphy2](https://github.com/kmike/pymorphy2) packages) The fine-tuning was performed on a [Leipzig web corpus](https://wortschatz.uni-leipzig.de/en/download/Russian) of Russian sentences. The model can be applied as follows: ``` # !pip install transformers sentencepiece import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small-normalizer") model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small-normalizer") text = 'меня тобой не понимать' inputs = tokenizer(text, return_tensors='pt') with torch.no_grad(): hypotheses = model.generate( **inputs, do_sample=True, top_p=0.95, num_return_sequences=5, repetition_penalty=2.5, max_length=32, ) for h in hypotheses: print(tokenizer.decode(h, skip_special_tokens=True)) ``` A possible output is: ``` # Мне тебя не понимать. # Если бы ты понимаешь меня? # Я с тобой не понимаю. # Я тебя не понимаю. # Я не понимаю о чем ты. ```
cointegrated/rut5-small
2021-04-28T07:09:45.000Z
[ "pytorch", "mt5", "seq2seq", "ru", "transformers", "paraphrasing", "russian", "license:mit", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "README.md", "config.json", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tokenizer_config.json" ]
cointegrated
598
transformers
--- language: "ru" tags: - paraphrasing - russian license: mit --- This is a small Russian paraphraser based on the [google/mt5-small](https://huggingface.co/google/mt5-small) model. It has rather poor paraphrasing performance, but can be fine tuned for this or other tasks. This model was created by taking the [alenusch/mt5small-ruparaphraser](https://huggingface.co/alenusch/mt5small-ruparaphraser) model and stripping 96% of its vocabulary which is unrelated to the Russian language or infrequent. * The original model has 300M parameters, with 256M of them being input and output embeddings. * After shrinking the `sentencepiece` vocabulary from 250K to 20K the number of model parameters reduced to 65M parameters, and model size reduced from 1.1GB to 246MB. * The first 5K tokens in the new vocabulary are taken from the original `mt5-small`. * The next 15K tokens are the most frequent tokens obtained by tokenizing a Russian web corpus from the [Leipzig corpora collection](https://wortschatz.uni-leipzig.de/en/download/Russian). The model can be used as follows: ``` # !pip install transformers sentencepiece import torch from transformers import T5ForConditionalGeneration, T5Tokenizer tokenizer = T5Tokenizer.from_pretrained("cointegrated/rut5-small") model = T5ForConditionalGeneration.from_pretrained("cointegrated/rut5-small") text = 'Ехал Грека через реку, видит Грека в реке рак. ' inputs = tokenizer(text, return_tensors='pt') with torch.no_grad(): hypotheses = model.generate( **inputs, do_sample=True, top_p=0.95, num_return_sequences=10, repetition_penalty=2.5, max_length=32, ) for h in hypotheses: print(tokenizer.decode(h, skip_special_tokens=True)) ```
colorfulscoop/gpt2-small-ja
2021-05-21T15:04:03.000Z
[ "pytorch", "tf", "jax", "gpt2", "lm-head", "causal-lm", "ja", "dataset:wikipedia", "transformers", "license:cc-by-sa-3.0", "text-generation" ]
text-generation
[ ".gitattributes", "CHANGELOG.md", "README.md", "config.json", "flax_model.msgpack", "pytorch_model.bin", "special_tokens_map.json", "spiece.model", "tf_model.h5", "tokenizer_config.json" ]
colorfulscoop
625
transformers
--- language: ja datasets: wikipedia widget: - text: "近年の機械学習は" license: cc-by-sa-3.0 --- # GPT-2 small Japanese model This repository contains a pretrained SentencePiece tokenizer model and GPT-2 small model trained on Japanese Wikipedia dataset. ## Training data [Japanese Wikipedia](https://ja.wikipedia.org/wiki/Wikipedia:データベースダウンロード) dataset which is released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) is used for training both the tokenizer and GPT-2 model as of March 1st, 2021. The dataset is splitted into three subsets - train, valid and test. Both of tokenizer and model are trained with the train split. ## Model description The model architecture is the same as GPT-2 small model (n_ctx: 1024, n_embd 768, n_head: 12, n_layer: 12) except for a vocabulary size. The vocabulary size is set to 32,000 instead of an original size of 50,257. `transformers.GPT2LMHeadModel` is used for training. ## Tokenizer description [SentencePiece](https://github.com/google/sentencepiece) tokenizer is used as a tokenizer for this model. In a training, the tokenizer model is trained with 10,000,000 samples which are extracted from the train split of training data. The vocabulary size is set to 32,000. A `add_dummy_prefix` option is set to `True` because words are not separated by whitespaces in Japanese. After training, the model is imported to `transformers.BERTGenerationTokenizer` because it supports SentencePiece models and it does not add any special tokens as default, which is useful expecially for a text generation task. ## Training The model is trained on the train split for 10 epochs with batch size 2 and 1024 tokens for each sample (i.e. 2048 tokens are processed in each batch). Each epoch contains around 250,000 steps. Adam optimizer is used. The learning rate is linearly decreased from `1e-4` to `0`. A clip norm is also used to set to `1.0`. After finishing training, the training loss is reached to 3.23, wihle the validation loss is reached to 3.50. All the code to train tokenizer and GPT-2 models are available in [a repository on GitHub](https://github.com/colorfulscoop/tfdlg/tree/8d068f4cc3fac49555971ad8244a540587745d79/examples/transformers-gpt2-ja) ## Usage First, install dependecies. ```sh $ pip install transformers==4.3.3 torch==1.8.0 sentencepiece==0.1.91 ``` Then load the pretrained tokenizer and GPT-2 model, and call a `generate` method. ```sh >>> import transformers >>> tokenizer = transformers.AutoTokenizer.from_pretrained("colorfulscoop/gpt2-small-ja") >>> model = transformers.AutoModelForCausalLM.from_pretrained("colorfulscoop/gpt2-small-ja") >>> input = tokenizer.encode("近年の機械学習は", return_tensors="pt") >>> output = model.generate(input, do_sample=True, top_p=0.95, top_k=50, num_return_sequences=3) >>> tokenizer.batch_decode(output) ['近年の機械学習は、特に、コンピューター学習において重要な概念である。この概念は、教育心理学', '近年の機械学習は時間間隔の短縮、時間間隔の短縮、学習時間の短縮、学習の', '近年の機械学習は、学生と学生が自分の能力を高め、結果を向上させることを目的としている。それは、'] ``` **Note:** The default model configuration `config.json` sets some generation parameters with `do_sample=True`, `top_k=50`, `top_p=0.95`. Please reset these parameters when you need to set different parameters. ## License All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). **Disclaimer:** The model potentially has possibility that it generates similar texts in the training data, texts not to be true, or biased texts. Use of the model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output. **Author:** Colorful Scoop
congcongwang/bart-base-en-zh
2020-10-04T21:16:04.000Z
[ "pytorch", "bart", "seq2seq", "transformers", "text2text-generation" ]
text2text-generation
[ ".gitattributes", "config.json", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
congcongwang
74
transformers
congcongwang/distilgpt2_fine_tuned_coder
2021-05-21T15:04:51.000Z
[ "pytorch", "jax", "gpt2", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "added_tokens.json", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
congcongwang
86
transformers
congcongwang/gpt2_medium_fine_tuned_coder
2021-05-21T15:06:28.000Z
[ "pytorch", "jax", "gpt2", "lm-head", "causal-lm", "transformers", "text-generation" ]
text-generation
[ ".gitattributes", "added_tokens.json", "config.json", "flax_model.msgpack", "merges.txt", "pytorch_model.bin", "special_tokens_map.json", "tokenizer_config.json", "vocab.json" ]
congcongwang
138
transformers