librarian-bots/card_with_first_commit · Datasets at Hugging Face

modelId stringlengths 4 81	tags sequence	pipeline_tag stringclasses 17 values	config dict	downloads int64 0 59.7M	first_commit unknown	card stringlengths 51 438k
CAMeL-Lab/bert-base-arabic-camelbert-ca	[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	580	null	--- language: "en" tags: - English - Bible dataset: - English Bible Translation Dataset - Link: https://www.kaggle.com/oswinrh/bible inference: false --- ## Dataset English Bible Translation Dataset (https://www.kaggle.com/oswinrh/bible) NOTE: It is `roberta-base` fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 `.csv` files mentioned above, which consist of around 5.5M words. ## Citation If you use this model in your work, please add the following citation - ``` @inproceedings{nandy-etal-2021-cs60075, title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora", author = "Nandy, Abhilash and Adak, Sayantan and Halder, Tanurima and Pokala, Sai Mahesh", booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.semeval-1.87", doi = "10.18653/v1/2021.semeval-1.87", pages = "678--682", abstract = "The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).", } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-poetry	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:1905.05700", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	37	null	--- language: - English tags: - EManuals - customer support - QA - bert --- Refer to https://aclanthology.org/2021.findings-emnlp.392/ for the paper and https://sites.google.com/view/emanualqa/home for the project website ## Citation Please cite the work if you would like to use it. ``` @inproceedings{nandy-etal-2021-question-answering, title = "Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework", author = "Nandy, Abhilash and Sharma, Soumya and Maddhashiya, Shubham and Sachdeva, Kapil and Goyal, Pawan and Ganguly, NIloy", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.392", doi = "10.18653/v1/2021.findings-emnlp.392", pages = "4600--4609", abstract = "Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40{\%} in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.", } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	54	null	--- language: - English tags: - Europarl - roberta datasets: - Europarl --- Refer to https://aclanthology.org/2021.semeval-1.87/ ## Citation If you use this model in your work, please add the following citation - ``` @inproceedings{nandy-etal-2021-cs60075, title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora", author = "Nandy, Abhilash and Adak, Sayantan and Halder, Tanurima and Pokala, Sai Mahesh", booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.semeval-1.87", doi = "10.18653/v1/2021.semeval-1.87", pages = "678--682", abstract = "The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).", } ```
CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "has_space" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	19,850	null	--- language: en tags: - spanbert datasets: - ade_corpus_v2 widget: - text: "Having fever after taking paracetamol." example_title: "NER" - text: "Birth defects associated with thalidomide." example_title: "NER" - text: "Deafness and kidney failure associated with gentamicin (an antibiotic)." example_title: "NER" - text: "Bleeding of the intestine associated with aspirin therapy." example_title: "NER" --- spanbert-large-cased fine-tuned for <b>"Adverse drug reaction"</b> and <b>"Drug"</b> span Extraction. <b>Details of spanbert-large-cased:</b> https://huggingface.co/SpanBERT/spanbert-large-cased <b>Details of the downstream task (Adverse drug reaction and Drug Extraction) - Dataset</b> https://huggingface.co/datasets/ade_corpus_v2
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	45	null	# Dataset --- --- datasets: - covid_qa_deepset --- --- Covid 19 question answering data obtained from [covid_qa_deepset](https://huggingface.co/datasets/covid_qa_deepset). # Original Repository Repository for the fine tuning, inference and evaluation scripts can be found [here](https://github.com/abhijithneilabraham/Covid-QA). # Model in action ``` import torch from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("abhijithneilabraham/longformer_covid_qa") model = AutoModelForQuestionAnswering.from_pretrained("abhijithneilabraham/longformer_covid_qa") question = "In this way, what do the mRNA-destabilising RBPs constitute ?" text = """ In this way, mRNA-destabilising RBPs constitute a 'brake' on the immune system, which may ultimately be toggled therapeutically. I anticipate continued efforts in this area will lead to new methods of regaining control over inflammation in autoimmunity, selectively enhancing immunity in immunotherapy, and modulating RNA synthesis and virus replication during infection. Another mRNA under post-transcriptional regulation by Regnase-1 and Roquin is Furin, which encodes a conserved proprotein convertase crucial in human health and disease. Furin, along with other PCSK family members, is widely implicated in immune regulation, cancer and the entry, maturation or release of a broad array of evolutionarily diverse viruses including human papillomavirus (HPV), influenza (IAV), Ebola (EboV), dengue (DenV) and human immunodeficiency virus (HIV). Here, Braun and Sauter review the roles of furin in these processes, as well as the history and future of furin-targeting therapeutics. 7 They also discuss their recent work revealing how two IFN-cinducible factors exhibit broad-spectrum inhibition of IAV, measles (MV), zika (ZikV) and HIV by suppressing furin activity. 8 Over the coming decade, I expect to see an ever-finer spatiotemporal resolution of host-oriented therapies to achieve safe, effective and broad-spectrum yet costeffective therapies for clinical use. The increasing abundance of affordable, sensitive, high-throughput genome sequencing technologies has led to a recent boom in metagenomics and the cataloguing of the microbiome of our world. The MinION nanopore sequencer is one of the latest innovations in this space, enabling direct sequencing in a miniature form factor with only minimal sample preparation and a consumer-grade laptop computer. Nakagawa and colleagues here report on their latest experiments using this system, further improving its performance for use in resource-poor contexts for meningitis diagnoses. 9 While direct sequencing of viral genomic RNA is challenging, this system was recently used to directly sequence an RNA virus genome (IAV) for the first time. 10 I anticipate further improvements in the performance of such devices over the coming decade will transform virus surveillance efforts, the importance of which was underscored by the recent EboV and novel coronavirus (nCoV / COVID-19) outbreaks, enabling rapid deployment of antiviral treatments that take resistance-conferring mutations into account. Decades of basic immunology research have provided a near-complete picture of the main armaments in the human antiviral arsenal. Nevertheless, this focus on mammalian defences and pathologies has sidelined examination of the types and roles of viruses and antiviral defences that exist throughout our biosphere. One case in point is the CRISPR/Cas antiviral immune system of prokaryotes, which is now repurposed as a revolutionary gene-editing biotechnology in plants and animals. 11 Another is the ancient lineage of nucleocytosolic large DNA viruses (NCLDVs), which are emerging human pathogens that possess enormous genomes of up to several megabases in size encoding hundreds of proteins with unique and unknown functions. 12 Moreover, hundreds of human-and avian-infective viruses such as IAV strain H5N1 are known, but recent efforts indicate the true number may be in the millions and many harbour zoonotic potential. 13 It is increasingly clear that host-virus interactions have generated truly vast yet poorly understood and untapped biodiversity. Closing this Special Feature, Watanabe and Kawaoka elaborate on neo-virology, an emerging field engaged in cataloguing and characterising this biodiversity through a global consortium. 14 I predict these efforts will unlock a vast wealth of currently unexplored biodiversity, leading to biotechnologies and treatments that leverage the host-virus interactions developed throughout evolution. When biomedical innovations fall into the 'Valley of Death', patients who are therefore not reached all too often fall with them. Being entrusted with the resources and expectation to conceive, deliver and communicate dividends to society is both cherished and eagerly pursued at every stage of our careers. Nevertheless, the road to research translation is winding and is built on a foundation of basic research. Supporting industry-academia collaboration and nurturing talent and skills in the Indo-Pacific region are two of the four pillars of the National Innovation and Science Agenda. 2 These frame Australia's Medical Research and Innovation Priorities, which include antimicrobial resistance, global health and health security, drug repurposing and translational research infrastructure, 15 capturing many of the key elements of this CTI Special Feature. Establishing durable international relationships that integrate diverse expertise is essential to delivering these outcomes. To this end, NHMRC has recently taken steps under the International Engagement Strategy 16 to increase cooperation with its counterparts overseas. These include the Japan Agency for Medical Research and Development (AMED), tasked with translating the biomedical research output of that country. Given the reciprocal efforts at accelerating bilateral engagement currently underway, 17 the prospects for new areas of international cooperation and mobility have never been more exciting nor urgent. With the above in mind, all contributions to this CTI Special Feature I have selected from research presented by fellow invitees to the 2018 Awaji International Forum on Infection and Immunity (AIFII) and 2017 Consortium of Biological Sciences (ConBio) conferences in Japan. Both Australia and Japan have strong traditions in immunology and related disciplines, and I predict that the quantity, quality and importance of our bilateral cooperation will accelerate rapidly over the short to medium term. By expanding and cooperatively leveraging our respective research strengths, our efforts may yet solve the many pressing disease, cost and other sustainability issues of our time. """ encoding = tokenizer(question, text, return_tensors="pt") input_ids = encoding["input_ids"] # default is local attention everywhere # the forward method will automatically set global attention on question tokens attention_mask = encoding["attention_mask"] start_scores, end_scores = model(input_ids, attention_mask=attention_mask) all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist()) answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1] answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) # output => a 'brake' on the immune system ```
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	63	null	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased') model = AutoModel.from_pretrained('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 25, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 900, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf	[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	132	null	--- language: - en license: apache-2.0 datasets: - squad_v2 model-index: - name: abhilash1910/albert-squad-v2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - type: exact_match value: 23.6563 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTE5ZTM2YzIwZjBhYjM0ZDUyNzBiMjg1YjZhMGJiMGViMjYzYjQ5ZmI4MGFkYmU4YjY1OTNjYzAwZWRlZjIwNSIsInZlcnNpb24iOjF9.jlvV8WRPSPKJm6UdApoh-dXcAOmLPtF5smsHt39RoO4sFzzbH6elUz5yPF5Lt9Yc2YDIl6c8JDsODqMxmsD0Bg - type: f1 value: 29.3808 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2ZjYWRlYTI1NDkwYzNhMzM5YTg2NjZmODg0NjNkOGM3YjM2NTlkYjVhZWI0MzlmNjNkMTMxODlkNTY3ODBkMiIsInZlcnNpb24iOjF9.CR1MYeU3uqld9bbI8CLupMtote4WEG9fIq9enwhFJfVpChIT9BGKm86zaPmXHg0yBaNHgkMaEt_a-DaIdiEwAg ---
CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment	[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	855	null	--- tags: - finance --- # Roberta Masked Language Model Trained On Financial Phrasebank Corpus This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a Financial Phrasebank Corpus. The model is built using Huggingface transformers. The model can be found at :[Financial_Roberta](https://huggingface.co/abhilash1910/financial_roberta) ## Specifications The corpus for training is taken from the Financial Phrasebank (Malo et al)[https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts]. ## Model Specification The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications: 1. vocab_size=56000 2. max_position_embeddings=514 3. num_attention_heads=12 4. num_hidden_layers=6 5. type_vocab_size=1 This is trained by using RobertaConfig from transformers package. The model is trained for 10 epochs with a gpu batch size of 64 units. ## Usage Specifications For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model. ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("abhilash1910/financial_roberta") model = AutoModelWithLMHead.from_pretrained("abhilash1910/financial_roberta") ``` After this the model will be downloaded, it will take some time to download all the model files. For testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows: ```python from transformers import pipeline model_mask = pipeline('fill-mask', model='abhilash1910/inancial_roberta') model_mask("The company had a <mask> of 20% in 2020.") ``` Some of the examples are also provided with generic financial statements: Example 1: ```python model_mask("The company had a <mask> of 20% in 2020.") ``` Output: ```bash [{'sequence': '<s>The company had a profit of 20% in 2020.</s>', 'score': 0.023112965747714043, 'token': 421, 'token_str': 'Ġprofit'}, {'sequence': '<s>The company had a loss of 20% in 2020.</s>', 'score': 0.021379893645644188, 'token': 616, 'token_str': 'Ġloss'}, {'sequence': '<s>The company had a year of 20% in 2020.</s>', 'score': 0.0185744296759367, 'token': 443, 'token_str': 'Ġyear'}, {'sequence': '<s>The company had a sales of 20% in 2020.</s>', 'score': 0.018143286928534508, 'token': 428, 'token_str': 'Ġsales'}, {'sequence': '<s>The company had a value of 20% in 2020.</s>', 'score': 0.015319528989493847, 'token': 776, 'token_str': 'Ġvalue'}] ``` Example 2: ```python model_mask("The <mask> is listed under NYSE") ``` Output: ```bash [{'sequence': '<s>The company is listed under NYSE</s>', 'score': 0.1566661298274994, 'token': 359, 'token_str': 'Ġcompany'}, {'sequence': '<s>The total is listed under NYSE</s>', 'score': 0.05542507395148277, 'token': 522, 'token_str': 'Ġtotal'}, {'sequence': '<s>The value is listed under NYSE</s>', 'score': 0.04729423299431801, 'token': 776, 'token_str': 'Ġvalue'}, {'sequence': '<s>The order is listed under NYSE</s>', 'score': 0.02533523552119732, 'token': 798, 'token_str': 'Ġorder'}, {'sequence': '<s>The contract is listed under NYSE</s>', 'score': 0.02087237872183323, 'token': 635, 'token_str': 'Ġcontract'}] ``` ## Resources For all resources , please look into the [HuggingFace](https://huggingface.co/) Site and the [Repositories](https://github.com/huggingface).
CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter	[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	12	null	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-ferd1 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 2652021 ## Validation Metrics - Loss: 0.3934604227542877 - Accuracy: 0.8411030860144452 - Precision: 0.8201550387596899 - Recall: 0.8076335877862595 - AUC: 0.8946767157983608 - F1: 0.8138461538461538 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-ferd1-2652021 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
CLTL/icf-levels-att	[ "pytorch", "roberta", "text-classification", "nl", "transformers", "license:mit" ]	text-classification	{ "architectures": [ "RobertaForSequenceClassification" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	32	null	Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on German using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz. As capitalization is an important part of the German language (eg. Sie vs. sie). I trained a model using a vocab that includes both lower case and upper case letters in hopes that the model would learn the correct casing. This removes the need to do any post-processing like truecasing. \| Reference \| Prediction \| \| ------------- \| ------------- \| \| Die zoologische Einordnung der Spezies ist seit Jahrzehnten umstritten \| Die psoologische Einordnung der Spezies ist seit Jahrzehnten umstritten \| \| Hauptgeschäftsfeld war ursprünglich der öffentliche Sektor in Irland \| Hauptgeschäftsfeld war ursprünglich der öffentliche Sektor in Irland \| \| Er vertrat den Wahlkreis Donauwörth im Parlament \| Er vertrat den Wahlkreis DonauWört im Parlament \| \| Ich bin gespannt welche Lieder sie wählt \| Ich bin gespannt welche Lieder see wählt \| \| Eine allgemein verbindliche Definition gibt es nicht \| Eine allgemeinverbindliche Definition gibt es nicht \| ``` from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import soundfile as sf import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("abnerh/wav2vec2-xlsr-300m-german-truecase") model = Wav2Vec2ForCTC.from_pretrained("abnerh/wav2vec2-xlsr-300m-german-truecase") speech, sr = sf.read('audio.wav') # tokenize input_values = processor(speech, return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) # print transcription results print(transcription) ```
CLTL/icf-levels-fac	[ "pytorch", "roberta", "text-classification", "nl", "transformers", "license:mit" ]	text-classification	{ "architectures": [ "RobertaForSequenceClassification" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	32	null	# Transferring Monolingual Model to Low-Resource Language: The Case Of Tigrinya: ## Proposed Method: <img src="data/proposed.png" height = "330" width ="760" > The proposed method transfers a mono-lingual Transformer model into new target language at lexical level by learning new token embeddings. All implementation in this repo uses XLNet as a source Transformer model, however, other Transformer models can also be used similarly. ## Main files: All files are IPython Notebook files which can be excuted simply in Google Colab. - train.ipynb : Fine-tunes XLNet (mono-lingual transformer) on new target language (Tigrinya) sentiment analysis dataset. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bSSrKE-TSphUyrNB2UWhFI-Bkoz0a5l0?usp=sharing) - test.ipynb : Evaluates the fine-tuned model on test data. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17R1lvRjxILVNk971vzZT79o2OodwaNIX?usp=sharing) - token_embeddings.ipynb : Trains a word2vec token embeddings for Tigrinya language. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hCtetAllAjBw28EVQkJFpiKdFtXmuxV7?usp=sharing) - process_Tigrinya_comments.ipynb : Extracts Tigrinya comments from mixed language contents. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1-ndLlBV-iLZNBW3Z8OfKAqUUCjvGbdZU?usp=sharing) - extract_YouTube_comments.ipynb : Downloads available comments from a YouTube channel ID. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1b7G85wHKe18y45JIDtvDJdO5dOkRmDdp?usp=sharing) - auto_labelling.ipynb : Automatically labels Tigrinya comments in to positive or negative sentiments based on [Emoji's sentiment](http://kt.ijs.si/data/Emoji_sentiment_ranking/). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wnZf7CBBCIr966vRUITlxKCrANsMPpV7?usp=sharing) ## Tigrinya Tokenizer: A [sentencepiece](https://github.com/google/sentencepiece) based tokenizer for Tigrinya has been released to the public and can be accessed as in the following: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("abryee/TigXLNet") tokenizer.tokenize("ዋዋዋው እዛ ፍሊም ካብተን ዘድንቀን ሓንቲ ኢያ ሞ ብጣዕሚ ኢና ነመስግን ሓንቲ ክብላ ደልየ ዘሎኹ ሓደራኣኹም ኣብ ጊዜኹም ተረክቡ") ## TigXLNet: A new general purpose transformer model for low-resource language Tigrinya is also released to the public and be accessed as in the following: from transformers import AutoConfig, AutoModel config = AutoConfig.from_pretrained("abryee/TigXLNet") config.d_head = 64 model = AutoModel.from_pretrained("abryee/TigXLNet", config=config) ## Evaluation: The proposed method is evaluated using two datasets: - A newly created sentiment analysis dataset for low-resource language (Tigriyna). <table> <tr> <td> <table> <thead> <tr> <th><sub>Models</sub></th> <th><sub>Configuration</sub></th> <th><sub>F1-Score</sub></th> </tr> </thead> <tbody> <tr> <td rowspan=3><sub>BERT</sub></td> <td rowspan=1><sub>+Frozen BERT weights</sub></td> <td><sub>54.91</sub></td> </tr> <tr> <td rowspan=1><sub>+Random embeddings</sub></td> <td><sub>74.26</sub></td> </tr> <tr> <td rowspan=1><sub>+Frozen token embeddings</sub></td> <td><sub>76.35</sub></td> </tr> <tr> <td rowspan=3><sub>mBERT</sub></td> <td rowspan=1><sub>+Frozen mBERT weights</sub></td> <td><sub>57.32</sub></td> </tr> <tr> <td rowspan=1><sub>+Random embeddings</sub></td> <td><sub>76.01</sub></td> </tr> <tr> <td rowspan=1><sub>+Frozen token embeddings</sub></td> <td><sub>77.51</sub></td> </tr> <tr> <td rowspan=3><sub>XLNet</sub></td> <td rowspan=1><sub>+Frozen XLNet weights</sub></td> <td><strong><sub>68.14</sub></strong></td> </tr> <tr> <td rowspan=1><sub>+Random embeddings</sub></td> <td><strong><sub>77.83</sub></strong></td> </tr> <tr> <td rowspan=1><sub>+Frozen token embeddings</sub></td> <td><strong><sub>81.62</sub></strong></td> </tr> </tbody> </table> </td> <td><img src="data/effect_of_dataset_size.png" alt="3" width = 480px height = 280px></td> </tr> </table> - Cross-lingual Sentiment dataset ([CLS](https://zenodo.org/record/3251672#.Xs65VzozbIU)). <table> <thead> <tr> <th rowspan=2><sub>Models</sub></th> <th rowspan=1 colspan=3><sub>English</sub></th> <th rowspan=1 colspan=3><sub>German</sub></th> <th rowspan=1 colspan=3><sub>French</sub></th> <th rowspan=1 colspan=3><sub>Japanese</sub></th> <th rowspan=2><sub>Average</sub></th> </tr> <tr> <th colspan=1><sub>Books</sub></th> <th colspan=1><sub>DVD</sub></th> <th colspan=1><sub>Music</sub></th> <th colspan=1><sub>Books</sub></th> <th colspan=1><sub>DVD</sub></th> <th colspan=1><sub>Music</sub></th> <th colspan=1><sub>Books</sub></th> <th colspan=1><sub>DVD</sub></th> <th colspan=1><sub>Music</sub></th> <th colspan=1><sub>Books</sub></th> <th colspan=1><sub>DVD</sub></th> <th colspan=1><sub>Music</sub></th> </tr> </thead> <tbody> <tr> <td colspan=1><sub>XLNet</sub></td> <td colspan=1><sub><strong>92.90</strong></sub></td> <td colspan=1><sub><strong>93.31</strong></sub></td> <td colspan=1><sub><strong>92.02</strong></sub></td> <td colspan=1><sub>85.23</sub></td> <td colspan=1><sub>83.30</sub></td> <td colspan=1><sub>83.89</sub></td> <td colspan=1><sub>73.05</sub></td> <td colspan=1><sub>69.80</sub></td> <td colspan=1><sub>70.12</sub></td> <td colspan=1><sub>83.20</sub></td> <td colspan=1><sub><strong>86.07</strong></sub></td> <td colspan=1><sub>85.24</sub></td> <td colspan=1><sub>83.08</sub></td> </tr> <tr> <td colspan=1><sub>mBERT</sub></td> <td colspan=1><sub>92.78</sub></td> <td colspan=1><sub>90.30</sub></td> <td colspan=1><sub>91.88</sub></td> <td colspan=1><sub><strong>88.65</strong></sub></td> <td colspan=1><sub><strong>85.85</strong></sub></td> <td colspan=1><sub><strong>90.38</strong></sub></td> <td colspan=1><sub><strong>91.09</strong></sub></td> <td colspan=1><sub><strong>88.57</strong></sub></td> <td colspan=1><sub><strong>93.67</strong></sub></td> <td colspan=1><sub><strong>84.35</strong></sub></td> <td colspan=1><sub>81.77</sub></td> <td colspan=1><sub><strong>87.53</strong></sub></td> <td colspan=1><sub><strong>88.90</strong></sub></td> </tr> </tbody> </table> ## Dataset used for this paper: We have constructed new sentiment analysis dataset for Tigrinya language and it can be found in the zip file (Tigrinya Sentiment Analysis Dataset) ## Citing our paper: Our paper can be accessed from ArXiv [link](https://arxiv.org/pdf/2006.07698.pdf), and please consider citing our work. @misc{tela2020transferring, title={Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya}, author={Abrhalei Tela and Abraham Woubie and Ville Hautamaki}, year={2020}, eprint={2006.07698}, archivePrefix={arXiv}, primaryClass={cs.CL} } ## Any questions, comments, feedback is appreciated! And can be forwarded to the following email: [email protected]
CallumRai/HansardGPT2	[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	14	null	ruGPT-3 fine-tuned on russian fanfiction about Bangatan Boys (BTS).
Cameron/BERT-eec-emotion	[ "pytorch", "jax", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	36	null	https://www.guilded.gg/thisiscineplex/overview/news/XRz48Dr6 https://www.guilded.gg/FLIXmasGR/overview/news/7R0WorPy https://www.guilded.gg/FLIXmasGR/overview/news/NyE5BPmy https://www.guilded.gg/FLIXmasGR/overview/news/2l3Konal https://www.guilded.gg/FLIXmasGR/overview/news/AykDjvVR https://www.guilded.gg/FLIXmasGR/overview/news/16YOGQoR https://www.guilded.gg/FLIXmasGR/overview/news/KR2ngpXR https://www.guilded.gg/FLIXmasGR/overview/news/xypa2qZR https://www.guilded.gg/FLIXmasGR/overview/news/A6jZGQk6 https://www.guilded.gg/FLIXmasGR/overview/news/1ROQVMe6 https://www.guilded.gg/FLIXmasGR/overview/news/4yAW0Kvl https://www.guilded.gg/FLIXmasGR/overview/news/JlaoGQBy https://www.guilded.gg/FLIXmasGR/overview/news/YyrPnVEl https://www.guilded.gg/FLIXmasGR/overview/news/4lGz3aBR https://www.guilded.gg/FLIXmasGR/overview/news/16nKkj1y https://www.guilded.gg/FLIXmasGR/overview/news/X6QA0Ng6 https://www.guilded.gg/FLIXmasGR/overview/news/XRz4xGa6 https://www.guilded.gg/FLIXmasGR/overview/news/PlqV9826 https://www.guilded.gg/FLIXmasGR/overview/news/7R0WokWy https://www.guilded.gg/FLIXmasGR/overview/news/qlDvK4dy https://www.guilded.gg/FLIXmasGR/overview/news/2l3KopZl https://www.guilded.gg/FLIXmasGR/overview/news/16YOGj4R https://www.guilded.gg/FLIXmasGR/overview/news/4ldxGzQl
Cameron/BERT-jigsaw-identityhate	[ "pytorch", "jax", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	37	null	# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). `BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_laptop") model = AutoModel.from_pretrained("activebus/BERT-DK_laptop") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```
Cameron/BERT-jigsaw-severetoxic	[ "pytorch", "jax", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	30	null	# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp. ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_rest") model = AutoModel.from_pretrained("activebus/BERT-DK_rest") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```
Cameron/BERT-mdgender-convai-binary	[ "pytorch", "jax", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	33	null	# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. `BERT-PT_*` addtionally uses SQuAD 1.1. ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_laptop") model = AutoModel.from_pretrained("activebus/BERT-PT_laptop") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```
Cameron/BERT-mdgender-convai-ternary	[ "pytorch", "jax", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	38	null	# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp. `BERT-PT_*` addtionally uses SQuAD 1.1. ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_rest") model = AutoModel.from_pretrained("activebus/BERT-PT_rest") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```
Cameron/BERT-mdgender-wizard	[ "pytorch", "jax", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	30	null	# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. Please visit https://github.com/howardhsu/BERT-for-RRC-ABSA for details. `BERT-XD_Review` is a cross-domain (beyond just `laptop` and `restaurant`) language model, where each example is from a single product / restaurant with the same rating, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on `bert-base-uncased`. The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers). ## Model Description The original model is from `BERT-base-uncased`. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-XD_Review") model = AutoModel.from_pretrained("activebus/BERT-XD_Review") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) `BERT_Review` is expected to have similar performance on domain-specific tasks (such as aspect extraction) as `BERT-DK`, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words). ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```
Cameron/BERT-rtgender-opgender-annotations	[ "pytorch", "jax", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	33	null	# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT_Review` is cross-domain (beyond just `laptop` and `restaurant`) language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on `bert-base-uncased`. The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers). ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT_Review") model = AutoModel.from_pretrained("activebus/BERT_Review") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) `BERT_Review` is expected to have similar performance on domain-specific tasks (such as aspect extraction) as `BERT-DK`, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words). ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```
Canadiancaleb/DialoGPT-small-walter	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	13	null	--- language: - pt --- This model was distilled from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) ## Usage ```python from transformers import AutoTokenizer # Or BertTokenizer from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads from transformers import AutoModel # or BertModel, for BERT without pretraining heads model = AutoModelForPreTraining.from_pretrained('adalbertojunior/distilbert-portuguese-cased') tokenizer = AutoTokenizer.from_pretrained('adalbertojunior/distilbert-portuguese-cased', do_lower_case=False) ``` You should fine tune it on your own data. It can achieve accuracy up to 99% relative to the original BERTimbau in some tasks.
Canadiancaleb/jessebot	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- language: - pt --- Image Captioning in Portuguese trained with ViT and GPT2 [DEMO](https://huggingface.co/spaces/adalbertojunior/image_captioning_portuguese) Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
Captain272/lstm	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets. email : [email protected]
dccuchile/albert-base-spanish-finetuned-mldoc	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	34	null	--- title: Twitter Sentiments emoji: 😍 colorFrom: yellow colorTo: blue sdk: streamlit app_file: app.py pinned: false --- # Configuration `title`: _string_ Display title for the Space `emoji`: _string_ Space emoji (emoji-only character allowed) `colorFrom`: _string_ Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) `colorTo`: _string_ Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) `sdk`: _string_ Can be either `gradio` or `streamlit` `app_file`: _string_ Path to your main application file (which contains either `gradio` or `streamlit` Python code). Path is relative to the root of the repository. `pinned`: _boolean_ Whether the Space stays on top of your list.
dccuchile/albert-base-spanish-finetuned-pawsx	[ "pytorch", "albert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	25	"2021-06-24T03:46:16Z"	--- license: apache-2.0 tags: - generated_from_trainer datasets: - null model_index: - name: 100perc results: - task: name: Causal Language Modeling type: text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 100perc This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4594 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| No log \| 1.0 \| 140 \| 1.8292 \| \| No log \| 2.0 \| 280 \| 1.7373 \| \| No log \| 3.0 \| 420 \| 1.6889 \| \| 2.26 \| 4.0 \| 560 \| 1.6515 \| \| 2.26 \| 5.0 \| 700 \| 1.6258 \| \| 2.26 \| 6.0 \| 840 \| 1.6063 \| \| 2.26 \| 7.0 \| 980 \| 1.5873 \| \| 1.6847 \| 8.0 \| 1120 \| 1.5749 \| \| 1.6847 \| 9.0 \| 1260 \| 1.5634 \| \| 1.6847 \| 10.0 \| 1400 \| 1.5513 \| \| 1.6073 \| 11.0 \| 1540 \| 1.5421 \| \| 1.6073 \| 12.0 \| 1680 \| 1.5352 \| \| 1.6073 \| 13.0 \| 1820 \| 1.5270 \| \| 1.6073 \| 14.0 \| 1960 \| 1.5203 \| \| 1.5545 \| 15.0 \| 2100 \| 1.5142 \| \| 1.5545 \| 16.0 \| 2240 \| 1.5089 \| \| 1.5545 \| 17.0 \| 2380 \| 1.5048 \| \| 1.5156 \| 18.0 \| 2520 \| 1.5009 \| \| 1.5156 \| 19.0 \| 2660 \| 1.4970 \| \| 1.5156 \| 20.0 \| 2800 \| 1.4935 \| \| 1.5156 \| 21.0 \| 2940 \| 1.4897 \| \| 1.4835 \| 22.0 \| 3080 \| 1.4865 \| \| 1.4835 \| 23.0 \| 3220 \| 1.4851 \| \| 1.4835 \| 24.0 \| 3360 \| 1.4820 \| \| 1.4565 \| 25.0 \| 3500 \| 1.4787 \| \| 1.4565 \| 26.0 \| 3640 \| 1.4774 \| \| 1.4565 \| 27.0 \| 3780 \| 1.4749 \| \| 1.4565 \| 28.0 \| 3920 \| 1.4748 \| \| 1.4326 \| 29.0 \| 4060 \| 1.4728 \| \| 1.4326 \| 30.0 \| 4200 \| 1.4692 \| \| 1.4326 \| 31.0 \| 4340 \| 1.4692 \| \| 1.4326 \| 32.0 \| 4480 \| 1.4668 \| \| 1.4126 \| 33.0 \| 4620 \| 1.4664 \| \| 1.4126 \| 34.0 \| 4760 \| 1.4659 \| \| 1.4126 \| 35.0 \| 4900 \| 1.4643 \| \| 1.394 \| 36.0 \| 5040 \| 1.4622 \| \| 1.394 \| 37.0 \| 5180 \| 1.4629 \| \| 1.394 \| 38.0 \| 5320 \| 1.4610 \| \| 1.394 \| 39.0 \| 5460 \| 1.4623 \| \| 1.3775 \| 40.0 \| 5600 \| 1.4599 \| \| 1.3775 \| 41.0 \| 5740 \| 1.4600 \| \| 1.3775 \| 42.0 \| 5880 \| 1.4580 \| \| 1.363 \| 43.0 \| 6020 \| 1.4584 \| \| 1.363 \| 44.0 \| 6160 \| 1.4577 \| \| 1.363 \| 45.0 \| 6300 \| 1.4559 \| \| 1.363 \| 46.0 \| 6440 \| 1.4545 \| \| 1.3484 \| 47.0 \| 6580 \| 1.4568 \| \| 1.3484 \| 48.0 \| 6720 \| 1.4579 \| \| 1.3484 \| 49.0 \| 6860 \| 1.4562 \| \| 1.3379 \| 50.0 \| 7000 \| 1.4558 \| \| 1.3379 \| 51.0 \| 7140 \| 1.4556 \| \| 1.3379 \| 52.0 \| 7280 \| 1.4581 \| \| 1.3379 \| 53.0 \| 7420 \| 1.4554 \| \| 1.3258 \| 54.0 \| 7560 \| 1.4561 \| \| 1.3258 \| 55.0 \| 7700 \| 1.4553 \| \| 1.3258 \| 56.0 \| 7840 \| 1.4555 \| \| 1.3258 \| 57.0 \| 7980 \| 1.4572 \| \| 1.3158 \| 58.0 \| 8120 \| 1.4551 \| \| 1.3158 \| 59.0 \| 8260 \| 1.4573 \| \| 1.3158 \| 60.0 \| 8400 \| 1.4561 \| \| 1.3072 \| 61.0 \| 8540 \| 1.4557 \| \| 1.3072 \| 62.0 \| 8680 \| 1.4548 \| \| 1.3072 \| 63.0 \| 8820 \| 1.4547 \| \| 1.3072 \| 64.0 \| 8960 \| 1.4556 \| \| 1.2986 \| 65.0 \| 9100 \| 1.4555 \| \| 1.2986 \| 66.0 \| 9240 \| 1.4566 \| \| 1.2986 \| 67.0 \| 9380 \| 1.4558 \| \| 1.2916 \| 68.0 \| 9520 \| 1.4565 \| \| 1.2916 \| 69.0 \| 9660 \| 1.4552 \| \| 1.2916 \| 70.0 \| 9800 \| 1.4558 \| \| 1.2916 \| 71.0 \| 9940 \| 1.4553 \| \| 1.2846 \| 72.0 \| 10080 \| 1.4579 \| \| 1.2846 \| 73.0 \| 10220 \| 1.4572 \| \| 1.2846 \| 74.0 \| 10360 \| 1.4572 \| \| 1.2792 \| 75.0 \| 10500 \| 1.4564 \| \| 1.2792 \| 76.0 \| 10640 \| 1.4576 \| \| 1.2792 \| 77.0 \| 10780 \| 1.4571 \| \| 1.2792 \| 78.0 \| 10920 \| 1.4580 \| \| 1.2736 \| 79.0 \| 11060 \| 1.4578 \| \| 1.2736 \| 80.0 \| 11200 \| 1.4583 \| \| 1.2736 \| 81.0 \| 11340 \| 1.4576 \| \| 1.2736 \| 82.0 \| 11480 \| 1.4580 \| \| 1.2699 \| 83.0 \| 11620 \| 1.4575 \| \| 1.2699 \| 84.0 \| 11760 \| 1.4583 \| \| 1.2699 \| 85.0 \| 11900 \| 1.4588 \| \| 1.2664 \| 86.0 \| 12040 \| 1.4590 \| \| 1.2664 \| 87.0 \| 12180 \| 1.4593 \| \| 1.2664 \| 88.0 \| 12320 \| 1.4582 \| \| 1.2664 \| 89.0 \| 12460 \| 1.4591 \| \| 1.2627 \| 90.0 \| 12600 \| 1.4595 \| \| 1.2627 \| 91.0 \| 12740 \| 1.4585 \| \| 1.2627 \| 92.0 \| 12880 \| 1.4590 \| \| 1.2613 \| 93.0 \| 13020 \| 1.4590 \| \| 1.2613 \| 94.0 \| 13160 \| 1.4598 \| \| 1.2613 \| 95.0 \| 13300 \| 1.4592 \| \| 1.2613 \| 96.0 \| 13440 \| 1.4597 \| \| 1.2591 \| 97.0 \| 13580 \| 1.4593 \| \| 1.2591 \| 98.0 \| 13720 \| 1.4593 \| \| 1.2591 \| 99.0 \| 13860 \| 1.4597 \| \| 1.258 \| 100.0 \| 14000 \| 1.4594 \| ### Framework versions - Transformers 4.8.0 - Pytorch 1.8.1+cu111 - Datasets 1.8.0 - Tokenizers 0.10.3
dccuchile/albert-xlarge-spanish-finetuned-ner	[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- language: - zh_CN - zh_CN license: mit tags: - generated_from_trainer metrics: - rouge model_index: - name: filter-mlsum-pretrained results: - task: name: Translation type: translation metric: name: Rouge1 type: rouge value: 42.1802 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # filter-mlsum-pretrained This model is a fine-tuned version of [lincoln/mbart-mlsum-automatic-summarization](https://huggingface.co/lincoln/mbart-mlsum-automatic-summarization) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 2.1258 - Rouge1: 42.1802 - Rouge2: 28.8282 - Rougel: 38.353 - Rougelsum: 38.4497 - Gen Len: 15.7048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Bleu \| Gen Len \| Validation Loss \| Rouge-1 \| Rouge-2 \| Rouge-3 \| Rouge-4 \| \|:-------------:\|:-----:\|:-----:\|:------:\|:-------:\|:---------------:\|:-------:\|:-------:\|:-------:\|:-------:\| \| 3.2488 \| 0.02 \| 600 \| 1.0077 \| 16.5021 \| 2.9137 \| 0.3472 \| 0.2187 \| 0.129 \| 0.0831 \| \| 2.8602 \| 0.04 \| 1200 \| 1.0448 \| 15.5959 \| 2.7929 \| 0.3555 \| 0.231 \| 0.1425 \| 0.0948 \| \| 2.7612 \| 0.06 \| 1800 \| 0.9912 \| 15.9283 \| 2.7275 \| 0.3634 \| 0.2327 \| 0.139 \| 0.0892 \| \| 2.71 \| 0.08 \| 2400 \| 1.1238 \| 16.029 \| 2.6673 \| 0.3705 \| 0.2393 \| 0.1448 \| 0.0937 \| \| 2.6029 \| 0.11 \| 3000 \| 1.091 \| 15.8317 \| 2.6153 \| 0.3705 \| 0.2382 \| 0.1443 \| 0.0943 \| \| 2.5834 \| 0.13 \| 3600 \| 1.0894 \| 15.9131 \| 2.5937 \| 0.3793 \| 0.246 \| 0.1517 \| 0.1013 \| \| 2.5339 \| 0.15 \| 4200 \| 1.1034 \| 15.8331 \| 2.5716 \| 0.3758 \| 0.2441 \| 0.146 \| 0.0948 \| \| 2.5176 \| 0.17 \| 4800 \| 1.1365 \| 16.2552 \| 2.5338 \| 0.3695 \| 0.2385 \| 0.1454 \| 0.0942 \| \| 2.4962 \| 0.19 \| 5400 \| 1.1237 \| 16.0041 \| 2.5145 \| 0.3773 \| 0.2462 \| 0.1533 \| 0.1017 \| \| 2.4573 \| 0.21 \| 6000 \| 0.9416 \| 16.1241 \| 2.5056 \| 0.3753 \| 0.2457 \| 0.1541 \| 0.1012 \| \| 2.4324 \| 0.23 \| 6600 \| 1.122 \| 15.3448 \| 2.4891 \| 0.3824 \| 0.2531 \| 0.157 \| 0.1033 \| \| 2.4343 \| 0.25 \| 7200 \| 1.8299 \| 15.5959 \| 2.4728 \| 0.384 \| 0.2512 \| 0.1556 \| 0.1026 \| \| 2.4089 \| 0.28 \| 7800 \| 1.7741 \| 16.3421 \| 2.4608 \| 0.3818 \| 0.2501 \| 0.1556 \| 0.102 \| \| 2.376 \| 0.3 \| 8400 \| 1.1575 \| 15.3834 \| 2.4402 \| 0.3887 \| 0.2582 \| 0.1611 \| 0.1058 \| \| 2.3739 \| 0.32 \| 9000 \| 1.7924 \| 15.6455 \| 2.4331 \| 0.3902 \| 0.2561 \| 0.1587 \| 0.1042 \| \| 2.3485 \| 0.34 \| 9600 \| 2.2605 \| 15.5407 \| 2.4215 \| 0.3712 \| 0.2423 \| 0.1493 \| 0.0984 \| \| 2.3535 \| 0.36 \| 10200 \| 1.2569 \| 16.2538 \| 2.4047 \| 0.3837 \| 0.2524 \| 0.1572 \| 0.1045 \| \| 2.3359 \| 0.38 \| 10800 \| 1.2334 \| 15.4607 \| 2.4025 \| 0.3808 \| 0.2488 \| 0.1531 \| 0.0994 \| \| 2.3265 \| 0.4 \| 11400 \| 1.116 \| 16.2703 \| 2.3926 \| 0.3909 \| 0.2574 \| 0.159 \| 0.1049 \| \| 2.3024 \| 0.42 \| 12000 \| 1.0944 \| 15.3807 \| 2.3964 \| 0.3883 \| 0.2554 \| 0.158 \| 0.1043 \| \| 2.2988 \| 0.45 \| 12600 \| 1.6318 \| 15.5062 \| 2.3616 \| 0.3889 \| 0.259 \| 0.1617 \| 0.107 \| \| 2.2966 \| 0.47 \| 13200 \| 1.1887 \| 15.8041 \| 2.3728 \| 0.3835 \| 0.2556 \| 0.1633 \| 0.1111 \| \| 2.2823 \| 0.49 \| 13800 \| 1.1252 \| 15.9972 \| 2.3591 \| 0.3805 \| 0.249 \| 0.1571 \| 0.1052 \| \| 2.2748 \| 0.51 \| 14400 \| 1.0418 \| 15.3021 \| 2.3619 \| 0.3862 \| 0.2569 \| 0.161 \| 0.1072 \| \| 2.2624 \| 0.53 \| 15000 \| 1.0299 \| 15.8634 \| 2.3415 \| 0.3909 \| 0.2575 \| 0.1608 \| 0.1072 \| \| 2.2585 \| 0.55 \| 15600 \| 1.0671 \| 15.5503 \| 2.3557 \| 0.3899 \| 0.2586 \| 0.1622 \| 0.1077 \| \| 2.2586 \| 0.57 \| 16200 \| 1.6521 \| 15.4345 \| 2.3431 \| 0.389 \| 0.2593 \| 0.1642 \| 0.1105 \| \| 2.2464 \| 0.59 \| 16800 \| 1.2836 \| 15.6124 \| 2.3609 \| 0.3934 \| 0.2591 \| 0.1593 \| 0.1041 \| \| 2.2523 \| 0.62 \| 17400 \| 1.7653 \| 15.8648 \| 2.3339 \| 0.3958 \| 0.2653 \| 0.1683 \| 0.1133 \| \| 2.2287 \| 0.64 \| 18000 \| 1.3186 \| 16.4455 \| 2.3188 \| 0.3911 \| 0.2617 \| 0.1678 \| 0.1143 \| \| 2.2068 \| 0.66 \| 18600 \| 1.6488 \| 15.9062 \| 2.3109 \| 0.3919 \| 0.262 \| 0.1657 \| 0.1115 \| \| 2.2195 \| 0.68 \| 19200 \| 1.8291 \| 15.5269 \| 2.3271 \| 0.3859 \| 0.2575 \| 0.1631 \| 0.1081 \| \| 2.2128 \| 0.7 \| 19800 \| 2.2759 \| 15.8703 \| 2.3113 \| 0.3962 \| 0.2655 \| 0.1691 \| 0.1123 \| \| 2.2071 \| 0.72 \| 20400 \| 2.4205 \| 15.9738 \| 2.3036 \| 0.3907 \| 0.2608 \| 0.1637 \| 0.1081 \| \| 2.1975 \| 0.74 \| 21000 \| 1.9886 \| 15.8234 \| 2.2919 \| 0.3906 \| 0.2632 \| 0.169 \| 0.1157 \| \| 2.1965 \| 0.76 \| 21600 \| 1.8754 \| 15.3434 \| 2.2957 \| 0.39 \| 0.2608 \| 0.1665 \| 0.1114 \| \| 2.1886 \| 0.78 \| 22200 \| 1.5683 \| 15.3407 \| 2.2835 \| 0.3968 \| 0.2658 \| 0.168 \| 0.1117 \| \| 2.185 \| 0.81 \| 22800 \| 2.127 \| 16.0566 \| 2.2685 \| 0.3913 \| 0.2624 \| 0.1691 \| 0.114 \| \| 2.1697 \| 0.83 \| 23400 \| 1.2554 \| 15.7021 \| 2.2888 \| 0.3983 \| 0.2676 \| 0.1704 \| 0.1148 \| \| 2.1637 \| 0.85 \| 24000 \| 2.0099 \| 16.2607 \| 2.2767 \| 0.3979 \| 0.2681 \| 0.1719 \| 0.1181 \| \| 2.1559 \| 0.87 \| 24600 \| 2.2632 \| 15.2179 \| 2.2840 \| 0.3996 \| 0.269 \| 0.1714 \| 0.1152 \| \| 2.1666 \| 0.89 \| 25200 \| 1.2354 \| 15.6828 \| 2.2744 \| 0.397 \| 0.2655 \| 0.1677 \| 0.1108 \| \| 2.1388 \| 0.91 \| 25800 \| 1.2576 \| 15.7959 \| 2.2661 \| 0.3982 \| 0.2655 \| 0.1687 \| 0.1128 \| \| 2.1458 \| 0.93 \| 26400 \| 1.334 \| 15.6428 \| 2.2582 \| 0.3976 \| 0.2682 \| 0.1711 \| 0.1142 \| \| 2.1337 \| 0.95 \| 27000 \| 1.287 \| 16.1379 \| 2.2474 \| 0.4001 \| 0.2654 \| 0.1682 \| 0.1119 \| \| 2.1324 \| 0.98 \| 27600 \| 1.1739 \| 16.0552 \| 2.2487 \| 0.4003 \| 0.2664 \| 0.168 \| 0.1113 \| \| 2.1318 \| 1.0 \| 28200 \| 2.1267 \| 15.931 \| 2.2553 \| 0.4037 \| 0.27 \| 0.1714 \| 0.1163 \| \| 2.0379 \| 1.02 \| 28800 \| 1.1489 \| 15.3421 \| 2.2787 \| 0.3962 \| 0.263 \| 0.1674 \| 0.114 \| \| 1.9044 \| 1.04 \| 29400 \| 1.6737 \| 15.6 \| 2.2538 \| 0.4003 \| 0.2693 \| 0.1729 \| 0.1161 \| \| 1.9149 \| 1.06 \| 30000 \| 1.1077 \| 15.771 \| 2.2487 \| 0.4062 \| 0.274 \| 0.1774 \| 0.1209 \| \| 1.9211 \| 1.08 \| 30600 \| 1.2744 \| 15.0566 \| 2.2708 \| 0.4075 \| 0.2742 \| 0.1744 \| 0.1172 \| \| 1.9285 \| 1.1 \| 31200 \| 1.1875 \| 16.1021 \| 2.2443 \| 0.3983 \| 0.2652 \| 0.1671 \| 0.1124 \| \| 1.9106 \| 1.12 \| 31800 \| 1.2422 \| 15.36 \| 2.2562 \| 0.4079 \| 0.2751 \| 0.1762 \| 0.119 \| \| 1.9313 \| 1.15 \| 32400 \| 1.3036 \| 15.8317 \| 2.2515 \| 0.4027 \| 0.2717 \| 0.1748 \| 0.1196 \| \| 1.931 \| 1.17 \| 33000 \| 1.138 \| 16.1917 \| 2.2415 \| 0.4016 \| 0.2701 \| 0.1724 \| 0.1179 \| \| 1.9232 \| 1.19 \| 33600 \| 1.2741 \| 15.6814 \| 2.2511 \| 0.4074 \| 0.2757 \| 0.1782 \| 0.1222 \| \| 1.9233 \| 1.21 \| 34200 \| 1.4101 \| 15.8345 \| 2.2388 \| 0.4027 \| 0.2712 \| 0.1727 \| 0.1174 \| \| 1.9172 \| 1.23 \| 34800 \| 1.252 \| 15.6124 \| 2.2434 \| 0.4046 \| 0.2747 \| 0.1783 \| 0.1215 \| \| 1.9258 \| 1.25 \| 35400 \| 1.2459 \| 15.5062 \| 2.2342 \| 0.4107 \| 0.2801 \| 0.1814 \| 0.1236 \| \| 1.9184 \| 1.27 \| 36000 \| 1.2943 \| 15.6083 \| 2.2393 \| 0.4119 \| 0.2817 \| 0.1839 \| 0.1244 \| \| 1.9195 \| 1.29 \| 36600 \| 1.1197 \| 15.8359 \| 2.2237 \| 0.4014 \| 0.2695 \| 0.1699 \| 0.1132 \| \| 1.932 \| 1.31 \| 37200 \| 1.2212 \| 15.9752 \| 2.2202 \| 0.4027 \| 0.2708 \| 0.1723 \| 0.1168 \| \| 1.9161 \| 1.34 \| 37800 \| 1.2541 \| 15.5779 \| 2.2236 \| 0.4091 \| 0.2783 \| 0.1804 \| 0.1244 \| \| 1.9115 \| 1.36 \| 38400 \| 1.4237 \| 15.8276 \| 2.1993 \| 0.4122 \| 0.2813 \| 0.1832 \| 0.1258 \| \| 1.9108 \| 1.38 \| 39000 \| 1.8321 \| 16.0386 \| 2.2079 \| 0.412 \| 0.2794 \| 0.1806 \| 0.1226 \| \| 1.921 \| 1.4 \| 39600 \| 1.8388 \| 15.5076 \| 2.2158 \| 0.411 \| 0.2799 \| 0.1804 \| 0.1226 \| \| 1.9124 \| 1.42 \| 40200 \| 1.915 \| 16.0 \| 2.2071 \| 0.4032 \| 0.2726 \| 0.1742 \| 0.1185 \| \| 1.9134 \| 1.44 \| 40800 \| 2.1237 \| 16.0372 \| 2.1980 \| 0.4036 \| 0.2702 \| 0.1689 \| 0.1122 \| \| 1.9124 \| 1.46 \| 41400 \| 2.4274 \| 15.3421 \| 2.2111 \| 0.4037 \| 0.274 \| 0.1754 \| 0.1203 \| \| 1.9149 \| 1.48 \| 42000 \| 1.8393 \| 15.5683 \| 2.2105 \| 0.4057 \| 0.2748 \| 0.1762 \| 0.119 \| \| 1.9147 \| 1.51 \| 42600 \| 1.2703 \| 16.3048 \| 2.1874 \| 0.4084 \| 0.2767 \| 0.179 \| 0.1233 \| \| 1.9075 \| 1.53 \| 43200 \| 1.7775 \| 15.9545 \| 2.1946 \| 0.4109 \| 0.2807 \| 0.1857 \| 0.1286 \| \| 1.8996 \| 1.55 \| 43800 \| 1.2485 \| 15.6648 \| 2.1924 \| 0.4082 \| 0.2749 \| 0.1764 \| 0.1196 \| \| 1.9003 \| 1.57 \| 44400 \| 1.1624 \| 15.8041 \| 2.1895 \| 0.4093 \| 0.2758 \| 0.1766 \| 0.1194 \| \| 1.9048 \| 1.59 \| 45000 \| 1.8167 \| 16.2938 \| 2.1843 \| 0.407 \| 0.2741 \| 0.1779 \| 0.1203 \| \| 1.9017 \| 1.61 \| 45600 \| 2.0689 \| 15.3931 \| 2.2073 \| 0.4111 \| 0.2795 \| 0.1811 \| 0.1246 \| \| 1.8946 \| 1.63 \| 46200 \| 1.7099 \| 15.9917 \| 2.1839 \| 0.4095 \| 0.2797 \| 0.1826 \| 0.1258 \| \| 1.886 \| 1.65 \| 46800 \| 1.8287 \| 15.8276 \| 2.1945 \| 0.4051 \| 0.2761 \| 0.1799 \| 0.1237 \| \| 1.9068 \| 1.68 \| 47400 \| 1.9476 \| 15.3503 \| 2.1926 \| 0.4132 \| 0.2819 \| 0.1836 \| 0.1262 \| \| 1.9008 \| 1.7 \| 48000 \| 1.3086 \| 15.5931 \| 2.1857 \| 0.4167 \| 0.2868 \| 0.1893 \| 0.1303 \| \| 1.8965 \| 1.72 \| 48600 \| 2.1687 \| 15.8317 \| 2.1781 \| 0.402 \| 0.2715 \| 0.175 \| 0.1197 \| \| 1.8907 \| 1.74 \| 49200 \| 2.3316 \| 15.8952 \| 2.1661 \| 0.4035 \| 0.2717 \| 0.1746 \| 0.1193 \| \| 1.8938 \| 1.76 \| 49800 \| 1.6839 \| 15.6028 \| 2.1736 \| 0.4008 \| 0.2693 \| 0.1741 \| 0.1184 \| \| 1.8769 \| 1.78 \| 50400 \| 1.1867 \| 15.9393 \| 2.1723 \| 0.403 \| 0.272 \| 0.1761 \| 0.1201 \| \| 1.8813 \| 1.8 \| 51000 \| 1.8509 \| 16.2538 \| 2.1454 \| 0.4085 \| 0.2773 \| 0.1801 \| 0.1227 \| \| 1.8913 \| 1.82 \| 51600 \| 1.9677 \| 15.7503 \| 2.1691 \| 0.4052 \| 0.2786 \| 0.1836 \| 0.1274 \| \| 1.8785 \| 1.85 \| 52200 \| 1.7 \| 15.7559 \| 2.1683 \| 0.4132 \| 0.2793 \| 0.1796 \| 0.1216 \| \| 1.881 \| 1.87 \| 52800 \| 1.2867 \| 16.0345 \| 2.1372 \| 0.416 \| 0.2824 \| 0.1837 \| 0.1264 \| \| 1.8833 \| 1.89 \| 53400 \| 1.761 \| 16.0966 \| 2.1501 \| 0.4126 \| 0.2808 \| 0.1825 \| 0.1253 \| \| 1.8727 \| 1.91 \| 54000 \| 1.9868 \| 15.8221 \| 2.1504 \| 0.4165 \| 0.2828 \| 0.1826 \| 0.1233 \| \| 1.8901 \| 1.93 \| 54600 \| 1.801 \| 14.9393 \| 2.2104 \| 0.4151 \| 0.2846 \| 0.1848 \| 0.1258 \| \| 1.8802 \| 1.95 \| 55200 \| 2.0887 \| 15.8069 \| 2.1555 \| 0.407 \| 0.2766 \| 0.1794 \| 0.1214 \| \| 1.8827 \| 1.97 \| 55800 \| 1.8323 \| 15.8524 \| 2.1510 \| 0.4221 \| 0.291 \| 0.193 \| 0.135 \| \| 1.8673 \| 1.99 \| 56400 \| 1.2667 \| 15.4262 \| 2.1620 \| 0.4092 \| 0.2795 \| 0.1836 \| 0.1275 \| \| 1.6735 \| 2.01 \| 57000 \| 1.821 \| 15.8538 \| 2.1836 \| 0.4193 \| 0.2875 \| 0.189 \| 0.1317 \| \| 1.6367 \| 2.04 \| 57600 \| 2.5547 \| 15.8055 \| 2.1941 \| 0.415 \| 0.2831 \| 0.1849 \| 0.1284 \| \| 1.6326 \| 2.06 \| 58200 \| 2.0999 \| 15.9352 \| 2.1743 \| 0.4157 \| 0.2829 \| 0.1842 \| 0.1267 \| \| 1.6354 \| 2.08 \| 58800 \| 2.3907 \| 15.68 \| 2.1879 \| 0.4233 \| 0.2921 \| 0.1936 \| 0.1361 \| \| 1.6352 \| 2.1 \| 59400 \| 1.979 \| 16.1807 \| 2.1735 \| 0.4236 \| 0.2907 \| 0.193 \| 0.1346 \| \| 1.6428 \| 2.12 \| 60000 \| 2.2266 \| 15.8759 \| 2.1858 \| 0.4204 \| 0.2881 \| 0.1896 \| 0.1308 \| \| 1.6483 \| 2.14 \| 60600 \| 1.9294 \| 15.8469 \| 2.1878 \| 0.4237 \| 0.2892 \| 0.1901 \| 0.1317 \| \| 1.6502 \| 2.16 \| 61200 \| 1.7967 \| 15.7131 \| 2.1814 \| 0.4164 \| 0.2835 \| 0.1852 \| 0.1275 \| \| 1.6585 \| 2.18 \| 61800 \| 1.1843 \| 16.0579 \| 2.1620 \| 0.413 \| 0.2828 \| 0.1852 \| 0.128 \| \| 1.6457 \| 2.21 \| 62400 \| 1.7951 \| 15.9862 \| 2.1873 \| 0.4194 \| 0.2885 \| 0.1908 \| 0.1341 \| \| 1.6433 \| 2.23 \| 63000 \| 1.6297 \| 16.1324 \| 2.1770 \| 0.4039 \| 0.2741 \| 0.1773 \| 0.1209 \| \| 1.6493 \| 2.25 \| 63600 \| 1.8762 \| 15.5131 \| 2.1702 \| 0.414 \| 0.2851 \| 0.1883 \| 0.1292 \| \| 1.672 \| 2.27 \| 64200 \| 2.1811 \| 16.1945 \| 2.1433 \| 0.4198 \| 0.2852 \| 0.1854 \| 0.1272 \| \| 1.6411 \| 2.29 \| 64800 \| 2.0637 \| 16.1434 \| 2.1661 \| 0.4103 \| 0.2809 \| 0.1848 \| 0.1275 \| \| 1.6561 \| 2.31 \| 65400 \| 2.452 \| 15.5724 \| 2.1761 \| 0.4204 \| 0.292 \| 0.1935 \| 0.135 \| \| 1.6516 \| 2.33 \| 66000 \| 2.216 \| 15.7048 \| 2.1836 \| 0.4186 \| 0.2887 \| 0.1909 \| 0.1326 \| \| 1.6738 \| 2.35 \| 66600 \| 1.7496 \| 15.731 \| 2.1452 \| 0.4186 \| 0.2904 \| 0.1944 \| 0.1364 \| \| 1.672 \| 2.38 \| 67200 \| 1.3179 \| 15.7697 \| 2.1412 \| 0.4206 \| 0.2898 \| 0.1936 \| 0.1358 \| \| 1.6625 \| 2.4 \| 67800 \| 2.3606 \| 15.76 \| 2.1412 \| 0.4134 \| 0.285 \| 0.189 \| 0.1315 \| \| 1.6725 \| 2.42 \| 68400 \| 2.3687 \| 15.4745 \| 2.1825 \| 0.4165 \| 0.2874 \| 0.1883 \| 0.1303 \| \| 1.6588 \| 2.44 \| 69000 \| 2.2056 \| 15.8841 \| 2.1307 \| 0.4259 \| 0.2952 \| 0.1974 \| 0.139 \| \| 1.6629 \| 2.46 \| 69600 \| 1.7605 \| 15.469 \| 2.1523 \| 0.4149 \| 0.2861 \| 0.1901 \| 0.1327 \| \| 1.6716 \| 2.48 \| 70200 \| 1.3733 \| 15.3683 \| 2.1546 \| 0.4202 \| 0.2889 \| 0.1897 \| 0.1314 \| \| 1.6708 \| 2.5 \| 70800 \| 2.6313 \| 15.7214 \| 2.1408 \| 0.4236 \| 0.2937 \| 0.1972 \| 0.1395 \| \| 1.6637 \| 2.52 \| 71400 \| 2.5112 \| 15.909 \| 2.1252 \| 0.4203 \| 0.2903 \| 0.1935 \| 0.1361 \| \| 1.6743 \| 2.55 \| 72000 \| 2.2902 \| 15.749 \| 2.1326 \| 0.426 \| 0.297 \| 0.1989 \| 0.1404 \| \| 1.6681 \| 2.57 \| 72600 \| 2.1003 \| 16.3338 \| 2.1120 \| 0.4185 \| 0.2876 \| 0.1904 \| 0.1342 \| \| 1.6791 \| 2.59 \| 73200 \| 1.7082 \| 15.7283 \| 2.1269 \| 0.4268 \| 0.2968 \| 0.1988 \| 0.1392 \| \| 1.6643 \| 2.61 \| 73800 \| 1.9914 \| 16.0552 \| 2.1166 \| 0.4177 \| 0.2886 \| 0.1939 \| 0.1369 \| \| 1.6666 \| 2.63 \| 74400 \| 1.8012 \| 16.0276 \| 2.1242 \| 0.4174 \| 0.2875 \| 0.19 \| 0.1328 \| \| 1.67 \| 2.65 \| 75000 \| 1.696 \| 15.5559 \| 2.1619 \| 0.4196 \| 0.2919 \| 0.1939 \| 0.136 \| \| 1.6794 \| 2.67 \| 75600 \| 2.0322 \| 15.6221 \| 2.1425 \| 0.4166 \| 0.2871 \| 0.1891 \| 0.1328 \| \| 1.6753 \| 2.69 \| 76200 \| 2.5736 \| 15.7407 \| 2.1432 \| 0.4215 \| 0.2928 \| 0.1958 \| 0.1388 \| \| 1.6807 \| 2.71 \| 76800 \| 2.3404 \| 15.7186 \| 2.1240 \| 0.4181 \| 0.2885 \| 0.1917 \| 0.1346 \| \| 1.6707 \| 2.74 \| 77400 \| 2.4439 \| 15.5724 \| 2.1246 \| 0.4191 \| 0.2906 \| 0.1936 \| 0.1359 \| \| 1.6736 \| 2.76 \| 78000 \| 2.0595 \| 16.2731 \| 2.1053 \| 0.4158 \| 0.2869 \| 0.1902 \| 0.1324 \| \| 1.6651 \| 2.78 \| 78600 \| 1.6489 \| 15.6772 \| 2.1365 \| 0.4242 \| 0.2924 \| 0.1938 \| 0.1346 \| \| 1.6746 \| 2.8 \| 79200 \| 1.1565 \| 15.9062 \| 2.1232 \| 0.4161 \| 0.2848 \| 0.1872 \| 0.1308 \| \| 1.6666 \| 2.82 \| 79800 \| 1.7445 \| 15.9407 \| 2.1417 \| 0.414 \| 0.2807 \| 0.1817 \| 0.1249 \| \| 1.6687 \| 2.84 \| 80400 \| 1.9425 \| 15.8676 \| 2.1240 \| 0.4088 \| 0.2786 \| 0.1821 \| 0.1269 \| \| 1.6678 \| 2.86 \| 81000 \| 1.6419 \| 15.9214 \| 2.1125 \| 0.417 \| 0.2873 \| 0.188 \| 0.1303 \| \| 1.6609 \| 2.88 \| 81600 \| 1.8123 \| 15.8579 \| 2.1227 \| 0.4199 \| 0.2904 \| 0.1916 \| 0.1323 \| ### Framework versions - Transformers 4.8.2 - Pytorch 1.8.1+cu111 - Datasets 1.9.0 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc	[ "pytorch", "bert", "text-classification", "transformers" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	39	"2021-07-07T18:25:16Z"	--- language: - zh_CN - zh_CN license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model_index: - name: tmp results: - task: name: Translation type: translation metric: name: Bleu type: bleu value: 0.0099 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # tmp This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: nan - Bleu: 0.0099 - Gen Len: 3.3917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1024 - eval_batch_size: 1024 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 1 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 2.0 \| 2 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 3.0 \| 3 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 4.0 \| 4 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 5.0 \| 5 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 6.0 \| 6 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 7.0 \| 7 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 8.0 \| 8 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 9.0 \| 9 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 10.0 \| 10 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 11.0 \| 11 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 12.0 \| 12 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 13.0 \| 13 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 14.0 \| 14 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 15.0 \| 15 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 16.0 \| 16 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 17.0 \| 17 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 18.0 \| 18 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 19.0 \| 19 \| nan \| 0.0114 \| 3.3338 \| \| No log \| 20.0 \| 20 \| nan \| 0.0114 \| 3.3338 \| ### Framework versions - Transformers 4.8.2 - Pytorch 1.8.1+cu111 - Datasets 1.9.0 - Tokenizers 0.10.3
dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner	[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	"2021-07-02T15:01:28Z"	--- license: mit tags: - generated_from_trainer datasets: - null model_index: - name: topicalchat-multiturn results: - task: name: Causal Language Modeling type: text-generation --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # topicalchat-multiturn This model is a fine-tuned version of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5260 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 73 \| 4.2992 \| \| No log \| 2.0 \| 146 \| 3.4433 \| \| No log \| 3.0 \| 219 \| 3.1606 \| \| No log \| 4.0 \| 292 \| 3.0366 \| \| No log \| 5.0 \| 365 \| 2.9679 \| \| No log \| 6.0 \| 438 \| 2.9131 \| \| 4.1401 \| 7.0 \| 511 \| 2.8752 \| \| 4.1401 \| 8.0 \| 584 \| 2.8391 \| \| 4.1401 \| 9.0 \| 657 \| 2.8118 \| \| 4.1401 \| 10.0 \| 730 \| 2.7871 \| \| 4.1401 \| 11.0 \| 803 \| 2.7659 \| \| 4.1401 \| 12.0 \| 876 \| 2.7489 \| \| 4.1401 \| 13.0 \| 949 \| 2.7331 \| \| 2.9768 \| 14.0 \| 1022 \| 2.7196 \| \| 2.9768 \| 15.0 \| 1095 \| 2.7071 \| \| 2.9768 \| 16.0 \| 1168 \| 2.6940 \| \| 2.9768 \| 17.0 \| 1241 \| 2.6854 \| \| 2.9768 \| 18.0 \| 1314 \| 2.6728 \| \| 2.9768 \| 19.0 \| 1387 \| 2.6647 \| \| 2.9768 \| 20.0 \| 1460 \| 2.6562 \| \| 2.7864 \| 21.0 \| 1533 \| 2.6482 \| \| 2.7864 \| 22.0 \| 1606 \| 2.6439 \| \| 2.7864 \| 23.0 \| 1679 \| 2.6326 \| \| 2.7864 \| 24.0 \| 1752 \| 2.6107 \| \| 2.7864 \| 25.0 \| 1825 \| 2.6043 \| \| 2.7864 \| 26.0 \| 1898 \| 2.5970 \| \| 2.7864 \| 27.0 \| 1971 \| 2.5908 \| \| 2.6568 \| 28.0 \| 2044 \| 2.5862 \| \| 2.6568 \| 29.0 \| 2117 \| 2.5828 \| \| 2.6568 \| 30.0 \| 2190 \| 2.5765 \| \| 2.6568 \| 31.0 \| 2263 \| 2.5742 \| \| 2.6568 \| 32.0 \| 2336 \| 2.5682 \| \| 2.6568 \| 33.0 \| 2409 \| 2.5656 \| \| 2.6568 \| 34.0 \| 2482 \| 2.5614 \| \| 2.5489 \| 35.0 \| 2555 \| 2.5605 \| \| 2.5489 \| 36.0 \| 2628 \| 2.5552 \| \| 2.5489 \| 37.0 \| 2701 \| 2.5541 \| \| 2.5489 \| 38.0 \| 2774 \| 2.5494 \| \| 2.5489 \| 39.0 \| 2847 \| 2.5491 \| \| 2.5489 \| 40.0 \| 2920 \| 2.5455 \| \| 2.5489 \| 41.0 \| 2993 \| 2.5452 \| \| 2.475 \| 42.0 \| 3066 \| 2.5433 \| \| 2.475 \| 43.0 \| 3139 \| 2.5397 \| \| 2.475 \| 44.0 \| 3212 \| 2.5386 \| \| 2.475 \| 45.0 \| 3285 \| 2.5400 \| \| 2.475 \| 46.0 \| 3358 \| 2.5339 \| \| 2.475 \| 47.0 \| 3431 \| 2.5327 \| \| 2.4144 \| 48.0 \| 3504 \| 2.5327 \| \| 2.4144 \| 49.0 \| 3577 \| 2.5312 \| \| 2.4144 \| 50.0 \| 3650 \| 2.5338 \| \| 2.4144 \| 51.0 \| 3723 \| 2.5314 \| \| 2.4144 \| 52.0 \| 3796 \| 2.5309 \| \| 2.4144 \| 53.0 \| 3869 \| 2.5289 \| \| 2.4144 \| 54.0 \| 3942 \| 2.5290 \| \| 2.3642 \| 55.0 \| 4015 \| 2.5270 \| \| 2.3642 \| 56.0 \| 4088 \| 2.5270 \| \| 2.3642 \| 57.0 \| 4161 \| 2.5263 \| \| 2.3642 \| 58.0 \| 4234 \| 2.5267 \| \| 2.3642 \| 59.0 \| 4307 \| 2.5273 \| \| 2.3642 \| 60.0 \| 4380 \| 2.5258 \| \| 2.3642 \| 61.0 \| 4453 \| 2.5253 \| \| 2.3216 \| 62.0 \| 4526 \| 2.5244 \| \| 2.3216 \| 63.0 \| 4599 \| 2.5256 \| \| 2.3216 \| 64.0 \| 4672 \| 2.5227 \| \| 2.3216 \| 65.0 \| 4745 \| 2.5241 \| \| 2.3216 \| 66.0 \| 4818 \| 2.5244 \| \| 2.3216 \| 67.0 \| 4891 \| 2.5236 \| \| 2.3216 \| 68.0 \| 4964 \| 2.5251 \| \| 2.2879 \| 69.0 \| 5037 \| 2.5231 \| \| 2.2879 \| 70.0 \| 5110 \| 2.5254 \| \| 2.2879 \| 71.0 \| 5183 \| 2.5242 \| \| 2.2879 \| 72.0 \| 5256 \| 2.5254 \| \| 2.2879 \| 73.0 \| 5329 \| 2.5253 \| \| 2.2879 \| 74.0 \| 5402 \| 2.5228 \| \| 2.2879 \| 75.0 \| 5475 \| 2.5247 \| \| 2.261 \| 76.0 \| 5548 \| 2.5243 \| \| 2.261 \| 77.0 \| 5621 \| 2.5247 \| \| 2.261 \| 78.0 \| 5694 \| 2.5250 \| \| 2.261 \| 79.0 \| 5767 \| 2.5248 \| \| 2.261 \| 80.0 \| 5840 \| 2.5236 \| \| 2.261 \| 81.0 \| 5913 \| 2.5264 \| \| 2.261 \| 82.0 \| 5986 \| 2.5249 \| \| 2.2396 \| 83.0 \| 6059 \| 2.5256 \| \| 2.2396 \| 84.0 \| 6132 \| 2.5267 \| \| 2.2396 \| 85.0 \| 6205 \| 2.5258 \| \| 2.2396 \| 86.0 \| 6278 \| 2.5242 \| \| 2.2396 \| 87.0 \| 6351 \| 2.5233 \| \| 2.2396 \| 88.0 \| 6424 \| 2.5249 \| \| 2.2396 \| 89.0 \| 6497 \| 2.5253 \| \| 2.2238 \| 90.0 \| 6570 \| 2.5252 \| \| 2.2238 \| 91.0 \| 6643 \| 2.5255 \| \| 2.2238 \| 92.0 \| 6716 \| 2.5263 \| \| 2.2238 \| 93.0 \| 6789 \| 2.5261 \| \| 2.2238 \| 94.0 \| 6862 \| 2.5257 \| \| 2.2238 \| 95.0 \| 6935 \| 2.5253 \| \| 2.213 \| 96.0 \| 7008 \| 2.5267 \| \| 2.213 \| 97.0 \| 7081 \| 2.5258 \| \| 2.213 \| 98.0 \| 7154 \| 2.5258 \| \| 2.213 \| 99.0 \| 7227 \| 2.5259 \| \| 2.213 \| 100.0 \| 7300 \| 2.5260 \| ### Framework versions - Transformers 4.8.1 - Pytorch 1.8.1+cu111 - Datasets 1.8.0 - Tokenizers 0.10.3
CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate	[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/hindi-wav2vec2-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/hindi-wav2vec2-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
Chaewon/mnmt_decoder_en_gpt2	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - itihasa model-index: - name: t5-base-finetuned-sn-to-en results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-sn-to-en This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the itihasa dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Chakita/Friends	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	"2021-12-19T16:52:53Z"	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/addy88/wav2vec2-assamese-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/addy88/wav2vec2-assamese-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
ChauhanVipul/BERT	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-punjabi-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-punjabi-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
Venkatakrishnan-Ramesh/Text_gen	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # aditeyabaral/sentencetransformer-roberta-base This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('aditeyabaral/sentencetransformer-roberta-base') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-roberta-base') model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-roberta-base') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-roberta-base) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 9234 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
ComCom/gpt2-large	[ "pytorch", "gpt2", "feature-extraction", "transformers" ]	feature-extraction	{ "architectures": [ "GPT2Model" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3
Contrastive-Tension/BERT-Distil-CT	[ "pytorch", "tf", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	null	--- tags: - conversational --- # Rick DialoGPT medium model
Contrastive-Tension/BERT-Distil-NLI-CT	[ "pytorch", "tf", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	6	null	distilbert-base-uncased finetuned on the conll2003 dataset for NER.
Crasher222/kaggle-comp-test	[ "pytorch", "bert", "text-classification", "en", "dataset:Crasher222/autonlp-data-kaggle-test", "transformers", "autonlp", "co2_eq_emissions" ]	text-classification	{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	29	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
Crumped/imdb-simpleRNN	[ "keras" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- language: en tags: - financial-sentiment-analysis - sentiment-analysis datasets: - financial_phrasebank widget: - text: Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales. - text: Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000. - text: Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008. --- ### FinancialBERT for Sentiment Analysis [FinancialBERT](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model. The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this model outperforms the general BERT and other financial domain-specific models. More details on `FinancialBERT`'s pre-training process can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining ### Training data FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). ### Fine-tuning hyper-parameters - learning_rate = 2e-5 - batch_size = 32 - max_seq_length = 512 - num_train_epochs = 5 ### Evaluation metrics The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set. \| sentiment \| precision \| recall \| f1-score \| support \| \| ------------- \|:-------------:\|:-------------:\|:-------------:\| -----:\| \| negative \| 0.96 \| 0.97 \| 0.97 \| 58 \| \| neutral \| 0.98 \| 0.99 \| 0.98 \| 279 \| \| positive \| 0.98 \| 0.97 \| 0.97 \| 148 \| \| macro avg \| 0.97 \| 0.98 \| 0.98 \| 485 \| \| weighted avg \| 0.98 \| 0.98 \| 0.98 \| 485 \| ### How to use The model can be used thanks to Transformers pipeline for sentiment analysis. ```python from transformers import BertTokenizer, BertForSequenceClassification from transformers import pipeline model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3) tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis") nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.", "Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000.", "Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008.", ] results = nlp(sentences) print(results) [{'label': 'positive', 'score': 0.9998133778572083}, {'label': 'neutral', 'score': 0.9997822642326355}, {'label': 'negative', 'score': 0.9877365231513977}] ``` > Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)
Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2-finetuned-de-to-is_nr2	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9260322366968425 - name: Recall type: recall value: 0.9383599955252265 - name: F1 type: f1 value: 0.9321553592265377 - name: Accuracy type: accuracy value: 0.9834146186474335 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0607 - Precision: 0.9260 - Recall: 0.9384 - F1: 0.9322 - Accuracy: 0.9834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.2545 \| 1.0 \| 878 \| 0.0711 \| 0.9096 \| 0.9214 \| 0.9154 \| 0.9800 \| \| 0.0555 \| 2.0 \| 1756 \| 0.0593 \| 0.9185 \| 0.9356 \| 0.9270 \| 0.9827 \| \| 0.0297 \| 3.0 \| 2634 \| 0.0607 \| 0.9260 \| 0.9384 \| 0.9322 \| 0.9834 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
DaWang/demo	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-wikinewssum-test results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mt5-small-wikinewssum-test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9354 - Rouge1: 6.8433 - Rouge2: 2.5498 - Rougel: 5.6114 - Rougelsum: 6.353 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| No log \| 1.0 \| 661 \| 3.2810 \| 6.4161 \| 2.403 \| 5.3674 \| 6.0329 \| \| No log \| 2.0 \| 1322 \| 3.1515 \| 6.9291 \| 2.6826 \| 5.6839 \| 6.4359 \| \| No log \| 3.0 \| 1983 \| 3.0565 \| 6.7939 \| 2.6113 \| 5.6133 \| 6.3126 \| \| No log \| 4.0 \| 2644 \| 2.9815 \| 6.0279 \| 2.1637 \| 4.9892 \| 5.5962 \| \| No log \| 5.0 \| 3305 \| 2.9645 \| 6.3926 \| 2.339 \| 5.2716 \| 5.9443 \| \| 3.9937 \| 6.0 \| 3966 \| 2.9476 \| 6.4739 \| 2.3615 \| 5.3473 \| 6.0089 \| \| 3.9937 \| 7.0 \| 4627 \| 2.9405 \| 6.615 \| 2.4309 \| 5.4493 \| 6.1445 \| \| 3.9937 \| 8.0 \| 5288 \| 2.9354 \| 6.8433 \| 2.5498 \| 5.6114 \| 6.353 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.1 - Datasets 1.16.1 - Tokenizers 0.10.3
Darkrider/covidbert_mednli	[ "transformers" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- language: th datasets: - common_voice tags: - audio - automatic-speech-recognition - hf-asr-leaderboard - robust-speech-event - speech - xlsr-fine-tuning license: cc-by-sa-4.0 model-index: - name: XLS-R-53 - Thai results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: th metrics: - name: Test WER type: wer value: 0.9524 - name: Test SER type: ser value: 1.2346 - name: Test CER type: cer value: 0.1623 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: sv metrics: - name: Test WER type: wer value: null - name: Test SER type: ser value: null - name: Test CER type: cer value: null --- # `wav2vec2-large-xlsr-53-th` Finetuning `wav2vec2-large-xlsr-53` on Thai [Common Voice 7.0](https://commonvoice.mozilla.org/en/datasets) [Read more on our blog](https://medium.com/airesearch-in-th/airesearch-in-th-3c1019a99cd) We finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) based on [Fine-tuning Wav2Vec2 for English ASR](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) using Thai examples of [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets). The notebooks and scripts can be found in [vistec-ai/wav2vec2-large-xlsr-53-th](https://github.com/vistec-ai/wav2vec2-large-xlsr-53-th). The pretrained model and processor can be found at [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). ## `robust-speech-event` Add `syllable_tokenize`, `word_tokenize` ([PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)) and [deepcut](https://github.com/rkcosmos/deepcut) tokenizers to `eval.py` from [robust-speech-event](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#evaluation) ``` > python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config th --split test --log_outputs --thai_tokenizer newmm/syllable/deepcut/cer ``` ### Eval results on Common Voice 7 "test": \| \| WER PyThaiNLP 2.3.1 \| WER deepcut \| SER \| CER \| \|---------------------------------\|---------------------\|-------------\|---------\|---------\| \| Only Tokenization \| 0.9524% \| 2.5316% \| 1.2346% \| 0.1623% \| \| Cleaning rules and Tokenization \| TBD \| TBD \| TBD \| TBD \| ## Usage ``` #load pretrained processor and model processor = Wav2Vec2Processor.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") model = Wav2Vec2ForCTC.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") #function to resample to 16_000 def speech_file_to_array_fn(batch, text_col="sentence", fname_col="path", resampling_to=16000): speech_array, sampling_rate = torchaudio.load(batch[fname_col]) resampler=torchaudio.transforms.Resample(sampling_rate, resampling_to) batch["speech"] = resampler(speech_array)[0].numpy() batch["sampling_rate"] = resampling_to batch["target_text"] = batch[text_col] return batch #get 2 examples as sample input 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) #infer with torch.no_grad(): logits = model(inputs.input_values,).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) >> Prediction: ['และ เขา ก็ สัมผัส ดีบุก', 'คุณ สามารถ รับทราบ เมื่อ ข้อความ นี้ ถูก อ่าน แล้ว'] >> Reference: ['และเขาก็สัมผัสดีบุก', 'คุณสามารถรับทราบเมื่อข้อความนี้ถูกอ่านแล้ว'] ``` ## Datasets Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) contains 133 validated hours of Thai (255 total hours) at 5GB. We pre-tokenize with `pythainlp.tokenize.word_tokenize`. We preprocess the dataset using cleaning rules described in `notebooks/cv-preprocess.ipynb` by [@tann9949](https://github.com/tann9949). We then deduplicate and split as described in [ekapolc/Thai_commonvoice_split](https://github.com/ekapolc/Thai_commonvoice_split) in order to 1) avoid data leakage due to random splits after cleaning in [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) and 2) preserve the majority of the data for the training set. The dataset loading script is `scripts/th_common_voice_70.py`. You can use this scripts together with `train_cleand.tsv`, `validation_cleaned.tsv` and `test_cleaned.tsv` to have the same splits as we do. The resulting dataset is as follows: ``` DatasetDict({ train: Dataset({ features: ['path', 'sentence'], num_rows: 86586 }) test: Dataset({ features: ['path', 'sentence'], num_rows: 2502 }) validation: Dataset({ features: ['path', 'sentence'], num_rows: 3027 }) }) ``` ## Training We fintuned using the following configuration on a single V100 GPU and chose the checkpoint with the lowest validation loss. The finetuning script is `scripts/wav2vec2_finetune.py` ``` # create model model = Wav2Vec2ForCTC.from_pretrained( "facebook/wav2vec2-large-xlsr-53", attention_dropout=0.1, hidden_dropout=0.1, feat_proj_dropout=0.0, mask_time_prob=0.05, layerdrop=0.1, gradient_checkpointing=True, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, vocab_size=len(processor.tokenizer) ) model.freeze_feature_extractor() training_args = TrainingArguments( output_dir="../data/wav2vec2-large-xlsr-53-thai", group_by_length=True, per_device_train_batch_size=32, gradient_accumulation_steps=1, per_device_eval_batch_size=16, metric_for_best_model='wer', evaluation_strategy="steps", eval_steps=1000, logging_strategy="steps", logging_steps=1000, save_strategy="steps", save_steps=1000, num_train_epochs=100, fp16=True, learning_rate=1e-4, warmup_steps=1000, save_total_limit=3, report_to="tensorboard" ) ``` ## Evaluation We benchmark on the test set using WER with words tokenized by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) 2.3.1 and [deepcut](https://github.com/rkcosmos/deepcut), and CER. We also measure performance when spell correction using [TNC](http://www.arts.chula.ac.th/ling/tnc/) ngrams is applied. Evaluation codes can be found in `notebooks/wav2vec2_finetuning_tutorial.ipynb`. Benchmark is performed on `test-unique` split. \| \| WER PyThaiNLP 2.3.1 \| WER deepcut \| CER \| \|--------------------------------\|---------------------\|----------------\|----------------\| \| [Kaldi from scratch](https://github.com/vistec-AI/commonvoice-th) \| 23.04 \| \| 7.57 \| \| Ours without spell correction \| 13.634024 \| 8.152052 \| 2.813019 \| \| Ours with spell correction \| 17.996397 \| 14.167975 \| 5.225761 \| \| Google Web Speech API※ \| 13.711234 \| 10.860058 \| 7.357340 \| \| Microsoft Bing Speech API※ \| 12.578819 \| 9.620991 \| 5.016620 \| \| Amazon Transcribe※ \| 21.86334 \| 14.487553 \| 7.077562 \| \| NECTEC AI for Thai Partii API※ \| 20.105887 \| 15.515631 \| 9.551027 \| ※ APIs are not finetuned with Common Voice 7.0 data ## LICENSE [cc-by-sa 4.0](https://github.com/vistec-AI/wav2vec2-large-xlsr-53-th/blob/main/LICENSE) ## Ackowledgements * model training and validation notebooks/scripts [@cstorm125](https://github.com/cstorm125/) * dataset cleaning scripts [@tann9949](https://github.com/tann9949) * dataset splits [@ekapolc](https://github.com/ekapolc/) and [@14mss](https://github.com/14mss) * running the training [@mrpeerat](https://github.com/mrpeerat) * spell correction [@wannaphong](https://github.com/wannaphong)
Darren/darren	[ "pytorch" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- widget: - text: "สวนกุหลาบเป็นโรงเรียนอะไร" context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย" --- # xlm-roberta-base-finetune-qa Finetuning `xlm-roberta-base` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`. Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py). Train with: ``` export WANDB_PROJECT=wangchanberta-qa export MODEL_NAME=xlm-roberta-base python train_question_answering_lm_finetuning.py \ --model_name $MODEL_NAME \ --dataset_name chimera_qa \ --output_dir $MODEL_NAME-finetune-chimera_qa-model \ --log_dir $MODEL_NAME-finetune-chimera_qa-log \ --pad_on_right \ --fp16 ```
DarshanDeshpande/marathi-distilbert	[ "pytorch", "tf", "distilbert", "fill-mask", "mr", "dataset:Oscar Corpus, News, Stories", "arxiv:1910.01108", "transformers", "license:apache-2.0", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "DistilBertForMaskedLM" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	14	null	# Finetuend `xlm-roberta-base` model on Thai sequence and token classification datasets <br> Finetuned XLM Roberta BASE model on Thai sequence and token classification datasets The script and documentation can be found at [this repository](https://github.com/vistec-AI/thai2transformers). <br> ## Model description <br> We use the pretrained cross-lingual RoBERTa model as proposed by [[Conneau et al., 2020]](https://arxiv.org/abs/1911.02116). We download the pretrained PyTorch model via HuggingFace's Model Hub (https://huggingface.co/xlm-roberta-base) <br> ## Intended uses & limitations <br> You can use the finetuned models for multiclass/multilabel text classification and token classification task. <br> Multiclass text classification - `wisesight_sentiment` 4-class text classification task (`positive`, `neutral`, `negative`, and `question`) based on social media posts and tweets. - `wongnai_reivews` Users' review rating classification task (scale is ranging from 1 to 5) - `generated_reviews_enth` : (`review_star` as label) Generated users' review rating classification task (scale is ranging from 1 to 5). Multilabel text classification - `prachathai67k` Thai topic classification with 12 labels based on news article corpus from prachathai.com. The detail is described in this [page](https://huggingface.co/datasets/prachathai67k). Token classification - `thainer` Named-entity recognition tagging with 13 named-entities as descibed in this [page](https://huggingface.co/datasets/thainer). - `lst20` : NER NER and POS tagging Named-entity recognition tagging with 10 named-entities and Part-of-Speech tagging with 16 tags as descibed in this [page](https://huggingface.co/datasets/lst20). <br> ## How to use <br> The example notebook demonstrating how to use finetuned model for inference can be found at this [Colab notebook](https://colab.research.google.com/drive/1Kbk6sBspZLwcnOE61adAQo30xxqOQ9ko) <br> BibTeX entry and citation info ``` @misc{lowphansirikul2021wangchanberta, title={WangchanBERTa: Pretraining transformer-based Thai Language Models}, author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong}, year={2021}, eprint={2101.09635}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
DataikuNLP/average_word_embeddings_glove.6B.300d	[ "arxiv:1908.10084", "sentence-transformers", "feature-extraction", "sentence-similarity", "license:apache-2.0" ]	sentence-similarity	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- tags: - conversational --- # Michael Scott DialoGPT Model
DataikuNLP/camembert-base	[ "pytorch", "tf", "camembert", "fill-mask", "fr", "dataset:oscar", "arxiv:1911.03894", "transformers", "license:mit", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "CamembertForMaskedLM" ], "model_type": "camembert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	null	--- tags: - conversational --- # Harry Potter DialoGPT Model
Davlan/byt5-base-eng-yor-mt	[ "pytorch", "t5", "text2text-generation", "arxiv:2103.08647", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "T5ForConditionalGeneration" ], "model_type": "t5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	11	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53-AL-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-AL-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5358 - Wer: 0.5443 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.9391 \| 0.4 \| 400 \| 2.0722 \| 0.9249 \| \| 0.8775 \| 0.8 \| 800 \| 1.7171 \| 0.6778 \| \| 0.665 \| 1.2 \| 1200 \| 1.7250 \| 0.6235 \| \| 0.6135 \| 1.6 \| 1600 \| 1.4021 \| 0.5847 \| \| 0.5795 \| 2.0 \| 2000 \| 1.6191 \| 0.5696 \| \| 0.5031 \| 2.4 \| 2400 \| 1.6767 \| 0.5586 \| \| 0.4933 \| 2.8 \| 2800 \| 1.5358 \| 0.5443 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
Davlan/byt5-base-yor-eng-mt	[ "pytorch", "t5", "text2text-generation", "arxiv:2103.08647", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "T5ForConditionalGeneration" ], "model_type": "t5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	12	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53-AL results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-AL This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2712 - Wer: 0.6940 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.073 \| 8.0 \| 200 \| 1.0990 \| 0.7002 \| \| 0.0561 \| 16.0 \| 400 \| 1.1455 \| 0.6805 \| \| 0.0378 \| 24.0 \| 600 \| 1.2712 \| 0.6940 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
Davlan/distilbert-base-multilingual-cased-masakhaner	[ "pytorch", "tf", "distilbert", "token-classification", "arxiv:2103.11811", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "DistilBertForTokenClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	16	null	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53-Total results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-Total This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2814 - Wer: 0.2260 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 2.9157 \| 0.2 \| 400 \| 2.8204 \| 0.9707 \| \| 0.9554 \| 0.4 \| 800 \| 0.5295 \| 0.5046 \| \| 0.7585 \| 0.6 \| 1200 \| 0.4007 \| 0.3850 \| \| 0.7288 \| 0.8 \| 1600 \| 0.3632 \| 0.3447 \| \| 0.6792 \| 1.0 \| 2000 \| 0.3433 \| 0.3216 \| \| 0.6085 \| 1.2 \| 2400 \| 0.3254 \| 0.2928 \| \| 0.6225 \| 1.4 \| 2800 \| 0.3161 \| 0.2832 \| \| 0.6183 \| 1.6 \| 3200 \| 0.3111 \| 0.2721 \| \| 0.5947 \| 1.8 \| 3600 \| 0.2969 \| 0.2615 \| \| 0.5953 \| 2.0 \| 4000 \| 0.2912 \| 0.2515 \| \| 0.5358 \| 2.2 \| 4400 \| 0.2920 \| 0.2501 \| \| 0.5535 \| 2.4 \| 4800 \| 0.2939 \| 0.2538 \| \| 0.5408 \| 2.6 \| 5200 \| 0.2854 \| 0.2452 \| \| 0.5272 \| 2.8 \| 5600 \| 0.2816 \| 0.2434 \| \| 0.5248 \| 3.0 \| 6000 \| 0.2755 \| 0.2354 \| \| 0.4923 \| 3.2 \| 6400 \| 0.2795 \| 0.2353 \| \| 0.489 \| 3.4 \| 6800 \| 0.2767 \| 0.2330 \| \| 0.4932 \| 3.6 \| 7200 \| 0.2821 \| 0.2335 \| \| 0.4841 \| 3.8 \| 7600 \| 0.2756 \| 0.2349 \| \| 0.4794 \| 4.0 \| 8000 \| 0.2751 \| 0.2265 \| \| 0.444 \| 4.2 \| 8400 \| 0.2809 \| 0.2283 \| \| 0.4533 \| 4.4 \| 8800 \| 0.2804 \| 0.2312 \| \| 0.4563 \| 4.6 \| 9200 \| 0.2830 \| 0.2256 \| \| 0.4498 \| 4.8 \| 9600 \| 0.2819 \| 0.2251 \| \| 0.4532 \| 5.0 \| 10000 \| 0.2814 \| 0.2260 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
Davlan/mt5_base_eng_yor_mt	[ "pytorch", "mt5", "text2text-generation", "arxiv:2103.08647", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MT5ForConditionalGeneration" ], "model_type": "mt5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	2	null	--- language: "id" widget: - text: "dia orang yang baik ya bunds." --- ## how to use ```python from transformers import pipeline, set_seed path = "akahana/indonesia-emotion-roberta" emotion = pipeline('text-classification', model=path,device=0) set_seed(42) kalimat = "dia orang yang baik ya bunds." preds = emotion(kalimat) preds [{'label': 'BAHAGIA', 'score': 0.8790940046310425}] ```
Davlan/xlm-roberta-base-finetuned-naija	[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "XLMRobertaForMaskedLM" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-tamil-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-tamil-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.8072 - Wer: 0.6531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 11.0967 \| 1.0 \| 118 \| 4.6437 \| 1.0 \| \| 3.4973 \| 2.0 \| 236 \| 3.2588 \| 1.0 \| \| 3.1305 \| 3.0 \| 354 \| 2.6566 \| 1.0 \| \| 1.2931 \| 4.0 \| 472 \| 0.9156 \| 0.9944 \| \| 0.6851 \| 5.0 \| 590 \| 0.7474 \| 0.8598 \| \| 0.525 \| 6.0 \| 708 \| 0.6649 \| 0.7995 \| \| 0.4325 \| 7.0 \| 826 \| 0.6740 \| 0.7752 \| \| 0.3766 \| 8.0 \| 944 \| 0.6220 \| 0.7628 \| \| 0.3256 \| 9.0 \| 1062 \| 0.6316 \| 0.7322 \| \| 0.2802 \| 10.0 \| 1180 \| 0.6442 \| 0.7305 \| \| 0.2575 \| 11.0 \| 1298 \| 0.6885 \| 0.7280 \| \| 0.2248 \| 12.0 \| 1416 \| 0.6702 \| 0.7197 \| \| 0.2089 \| 13.0 \| 1534 \| 0.6781 \| 0.7173 \| \| 0.1893 \| 14.0 \| 1652 \| 0.6981 \| 0.7049 \| \| 0.1652 \| 15.0 \| 1770 \| 0.7154 \| 0.7436 \| \| 0.1643 \| 16.0 \| 1888 \| 0.6798 \| 0.7023 \| \| 0.1472 \| 17.0 \| 2006 \| 0.7381 \| 0.6947 \| \| 0.1372 \| 18.0 \| 2124 \| 0.7240 \| 0.7065 \| \| 0.1318 \| 19.0 \| 2242 \| 0.7305 \| 0.6714 \| \| 0.1211 \| 20.0 \| 2360 \| 0.7288 \| 0.6597 \| \| 0.1178 \| 21.0 \| 2478 \| 0.7417 \| 0.6699 \| \| 0.1118 \| 22.0 \| 2596 \| 0.7476 \| 0.6753 \| \| 0.1016 \| 23.0 \| 2714 \| 0.7973 \| 0.6647 \| \| 0.0998 \| 24.0 \| 2832 \| 0.8027 \| 0.6633 \| \| 0.0917 \| 25.0 \| 2950 \| 0.8045 \| 0.6680 \| \| 0.0907 \| 26.0 \| 3068 \| 0.7884 \| 0.6565 \| \| 0.0835 \| 27.0 \| 3186 \| 0.8009 \| 0.6622 \| \| 0.0749 \| 28.0 \| 3304 \| 0.8123 \| 0.6536 \| \| 0.0755 \| 29.0 \| 3422 \| 0.8006 \| 0.6555 \| \| 0.074 \| 30.0 \| 3540 \| 0.8072 \| 0.6531 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
Davlan/xlm-roberta-large-masakhaner	[ "pytorch", "tf", "xlm-roberta", "token-classification", "arxiv:2103.11811", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "XLMRobertaForTokenClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1,449	null	--- language: en datasets: - cuad --- # Model Card for RoBERTa Large Model fine-tuned with CUAD dataset This model is the fine-tuned version of "RoBERTa Large" using CUAD dataset # Model Details ## Model Description The [Contract Understanding Atticus Dataset (CUAD)](https://www.atticusprojectai.org/cuad), pronounced "kwad", a dataset for legal contract review curated by the Atticus Project. Contract review is a task about "finding needles in a haystack." We find that Transformer models have nascent performance on CUAD, but that this performance is strongly influenced by model design and training dataset size. Despite some promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community. - Developed by: TheAtticusProject - Shared by [Optional]: HuggingFace - Model type: Language model - Language(s) (NLP): en - License: More information needed - Related Models: RoBERTA - Parent Model:RoBERTA Large - Resources for more information: - [GitHub Repo](https://github.com/TheAtticusProject/cuad) - [Associated Paper](https://arxiv.org/abs/2103.06268) # Uses ## Direct Use Legal contract review ## Downstream Use [Optional] More information needed ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations. # Training Details ## Training Data See [cuad dataset card](https://huggingface.co/datasets/cuad) for further details ## Training Procedure More information needed ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data #### Extra Data Researchers may be interested in several gigabytes of unlabeled contract pretraining data, which is available [here](https://drive.google.com/file/d/1of37X0hAhECQ3BN_004D8gm6V88tgZaB/view?usp=sharing). ### Factors More information needed ### Metrics More information needed ## Results We [provide checkpoints](https://zenodo.org/record/4599830) for three of the best models fine-tuned on CUAD: RoBERTa-base (~100M parameters), RoBERTa-large (~300M parameters), and DeBERTa-xlarge (~900M parameters). # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: More information needed - Hours used: More information needed - Cloud Provider: More information needed - Compute Region: More information needed - Carbon Emitted: More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software The HuggingFace [Transformers](https://huggingface.co/transformers) library. It was tested with Python 3.8, PyTorch 1.7, and Transformers 4.3/4.4. # Citation BibTeX: @article{hendrycks2021cuad, title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review}, author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball}, journal={NeurIPS}, year={2021} } # Glossary [optional] More information needed # More Information [optional] For more details about CUAD and legal contract review, see the [Atticus Project website](https://www.atticusprojectai.org/cuad). # Model Card Authors [optional] TheAtticusProject # Model Card Contact [TheAtticusProject](https://www.atticusprojectai.org/), in collaboration with the Ezi Ozoani and the HuggingFace Team # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("akdeniz27/roberta-large-cuad") model = AutoModelForQuestionAnswering.from_pretrained("akdeniz27/roberta-large-cuad") ``` </details>
Davlan/xlm-roberta-large-ner-hrl	[ "pytorch", "tf", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]	token-classification	{ "architectures": [ "XLMRobertaForTokenClassification" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	1,322	null	--- language: tr widget: - text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı." --- # Turkish Named Entity Recognition (NER) Model This model is the fine-tuned version of "xlm-roberta-base" (a multilingual version of RoBERTa) using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). # Fine-tuning parameters: ``` task = "ner" model_checkpoint = "xlm-roberta-base" batch_size = 8 label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] max_length = 512 learning_rate = 2e-5 num_train_epochs = 2 weight_decay = 0.01 ``` # How to use: ``` model = AutoModelForTokenClassification.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner") tokenizer = AutoTokenizer.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner") ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") ner("<your text here>") ``` Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. # Reference test results: * accuracy: 0.9919343118732742 * f1: 0.9492100796448622 * precision: 0.9407349896480332 * recall: 0.9578392621870883
Declan/CNN_model_v3	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- language: "ar" tags: - text-generation datasets: - Arabic poetry from several eras --- # GPT2-Small-Arabic-Poetry ## Model description Fine-tuned model of Arabic poetry dataset based on gpt2-small-arabic. ## Intended uses & limitations #### How to use An example is provided in this [colab notebook](https://colab.research.google.com/drive/1mRl7c-5v-Klx27EEAEOAbrfkustL4g7a?usp=sharing). #### Limitations and bias Both the GPT2-small-arabic (trained on Arabic Wikipedia) and this model have several limitations in terms of coverage and training performance. Use them as demonstrations or proof of concepts but not as production code. ## Training data This pretrained model used the [Arabic Poetry dataset](https://www.kaggle.com/ahmedabelal/arabic-poetry) from 9 different eras with a total of around 40k poems. The dataset was trained (fine-tuned) based on the [gpt2-small-arabic](https://huggingface.co/akhooli/gpt2-small-arabic) transformer model. ## Training procedure Training was done using [Simple Transformers](https://github.com/ThilinaRajapakse/simpletransformers) library on Kaggle, using free GPU. ## Eval results Final perplexity reached ws 76.3, loss: 4.33 ### BibTeX entry and citation info ```bibtex @inproceedings{Abed Khooli, year={2020} } ```
Declan/CNN_model_v5	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	"2020-09-06T15:15:36Z"	--- tags: - translation language: - ar - en license: mit --- ### mbart-large-ar-en This is mbart-large-cc25, finetuned on a subset of the OPUS corpus for ar_en. Usage: see [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing) Note: model has limited training set, not fully trained (do not use for production). Other models by me: [Abed Khooli](https://huggingface.co/akhooli)
Declan/FoxNews_model_v1	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: mit tags: - generated_from_trainer model-index: - name: conserv_fulltext_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # conserv_fulltext_model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3 unbalanced_texts gpt2
Declan/FoxNews_model_v8	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-gpt2 Changes: use old format for `pytorch_model.bin`.
Declan/HuffPost_model_v1	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mbart Changes: use old format for `pytorch_model.bin`.
Declan/HuffPost_model_v2	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mpnet Changes: use old format for `pytorch_model.bin`.
Declan/HuffPost_model_v4	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-xlnet Changes: use old format for `pytorch_model.bin`.
Declan/HuffPost_model_v8	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - matthews_correlation model_index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification metric: name: Matthews Correlation type: matthews_correlation value: 0.6290322580645161 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 1.0475 - Matthews Correlation: 0.6290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| No log \| 1.0 \| 16 \| 1.3863 \| 0.0 \| \| No log \| 2.0 \| 32 \| 1.2695 \| 0.4503 \| \| No log \| 3.0 \| 48 \| 1.1563 \| 0.6110 \| \| No log \| 4.0 \| 64 \| 1.0757 \| 0.6290 \| \| No log \| 5.0 \| 80 \| 1.0475 \| 0.6290 \| ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3
Declan/NPR_model_v2	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-cloud-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cloud-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0812 - Precision: 0.8975 - Recall: 0.9080 - F1: 0.9027 - Accuracy: 0.9703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 166 \| 0.1326 \| 0.7990 \| 0.8043 \| 0.8017 \| 0.9338 \| \| No log \| 2.0 \| 332 \| 0.0925 \| 0.8770 \| 0.8946 \| 0.8858 \| 0.9618 \| \| No log \| 3.0 \| 498 \| 0.0812 \| 0.8975 \| 0.9080 \| 0.9027 \| 0.9703 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
Declan/NPR_model_v3	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-cloud1-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cloud1-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0074 - Precision: 0.9714 - Recall: 0.9855 - F1: 0.9784 - Accuracy: 0.9972 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 166 \| 0.0160 \| 0.9653 \| 0.9420 \| 0.9535 \| 0.9945 \| \| No log \| 2.0 \| 332 \| 0.0089 \| 0.9623 \| 0.9855 \| 0.9737 \| 0.9965 \| \| No log \| 3.0 \| 498 \| 0.0074 \| 0.9714 \| 0.9855 \| 0.9784 \| 0.9972 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
Declan/NPR_model_v4	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-cloud2-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cloud2-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8866 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8453 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 162 \| 0.7804 \| 0.0 \| 0.0 \| 0.0 \| 0.8447 \| \| No log \| 2.0 \| 324 \| 0.8303 \| 0.0 \| 0.0 \| 0.0 \| 0.8465 \| \| No log \| 3.0 \| 486 \| 0.8866 \| 0.0 \| 0.0 \| 0.0 \| 0.8453 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
Declan/NPR_model_v6	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9988 - Precision: 0.3 - Recall: 0.6 - F1: 0.4 - Accuracy: 0.7870 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 84 \| 0.8399 \| 0.2105 \| 0.4 \| 0.2759 \| 0.75 \| \| No log \| 2.0 \| 168 \| 0.9664 \| 0.3 \| 0.6 \| 0.4 \| 0.7870 \| \| No log \| 3.0 \| 252 \| 0.9988 \| 0.3 \| 0.6 \| 0.4 \| 0.7870 \| ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0
Declan/WallStreetJournal_model_v2	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	7	null	--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - alecmullen/autonlp-data-group-classification co2_eq_emissions: 0.4362732160754736 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 441411446 - CO2 Emissions (in grams): 0.4362732160754736 ## Validation Metrics - Loss: 0.7598486542701721 - Accuracy: 0.8222222222222222 - Macro F1: 0.2912091747693842 - Micro F1: 0.8222222222222222 - Weighted F1: 0.7707160863181806 - Macro Precision: 0.29631463146314635 - Micro Precision: 0.8222222222222222 - Weighted Precision: 0.7341339689524508 - Macro Recall: 0.30174603174603176 - Micro Recall: 0.8222222222222222 - Weighted Recall: 0.8222222222222222 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/alecmullen/autonlp-group-classification-441411446 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```
DeltaHub/adapter_t5-3b_mrpc	[ "pytorch", "transformers" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 model-index: - name: t5-small-finetuned300-en-to-de results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned300-en-to-de This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 136 \| 1.1454 \| 14.2319 \| 17.8329 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
DeltaHub/lora_t5-base_mrpc	[ "pytorch", "transformers" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 model-index: - name: t5-small-finetuned8-en-to-de results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned8-en-to-de This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 136 \| 3.6717 \| 3.9127 \| 4.0207 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Deniskin/essays_small_2000	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- language: - ru multilinguality: - monolingual widget: - text: "Жалобы на боль внизу <mask> после приёма пищи." example_title: "pain_example" - text: "Пациентка наблюдалась у <mask> по поводу грибкового поражения кожи." example_title: "spec_example" - text: "Появился зуд тела, <mask> веса, потливость, проводил контроль сахара крови." example_title: "weight_example" --- Paper: https://arxiv.org/abs/2204.03951 Code: https://github.com/alexyalunin/RuBioRoBERTa ### Citation ``` @misc{alex2022rubioroberta, title={RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining}, author={Alexander Yalunin and Alexander Nesterov and Dmitriy Umerenkov}, year={2022}, eprint={2204.03951}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
Deniskin/gpt3_medium	[ "pytorch", "gpt2", "text-generation", "transformers", "has_space" ]	text-generation	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	52	null	--- language: - ar - dz tags: - pytorch - bert - multilingual - ar - dz license: apache-2.0 widget: - text: " أنا من الجزائر من ولاية [MASK] " - text: "rabi [MASK] khouya sami" - text: " ربي [MASK] خويا لعزيز" - text: "tahya el [MASK]." - text: "rouhi ya dzayer [MASK]" inference: true --- <img src="https://raw.githubusercontent.com/alger-ia/dziribert/main/dziribert_drawing.png" alt="drawing" width="25%" height="25%" align="right"/> # DziriBERT DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets). For more information, please visit our paper: https://arxiv.org/pdf/2109.12346.pdf. ## How to use ```python from transformers import BertTokenizer, BertForMaskedLM tokenizer = BertTokenizer.from_pretrained("alger-ia/dziribert") model = BertForMaskedLM.from_pretrained("alger-ia/dziribert") ``` You can find a fine-tuning script in our Github repo: https://github.com/alger-ia/dziribert ## Limitations The pre-training data used in this project comes from social media (Twitter). Therefore, the Masked Language Modeling objective may predict offensive words in some situations. Modeling this kind of words may be either an advantage (e.g. when training a hate speech model) or a disadvantage (e.g. when generating answers that are directly sent to the end user). Depending on your downstream task, you may need to filter out such words especially when returning automatically generated text to the end user. ### How to cite ```bibtex @article{dziribert, title={DziriBERT: a Pre-trained Language Model for the Algerian Dialect}, author={Abdaoui, Amine and Berrimi, Mohamed and Oussalah, Mourad and Moussaoui, Abdelouahab}, journal={arXiv preprint arXiv:2109.12346}, year={2021} } ``` ## Contact Please contact [email protected] for any question, feedback or request.
DeskDown/MarianMixFT_en-vi	[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased_token_itr0_0.0001_all_01_03_2022-04_48_27 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased_token_itr0_0.0001_all_01_03_2022-04_48_27 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2899 - Precision: 0.3170 - Recall: 0.5261 - F1: 0.3956 - Accuracy: 0.8799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 30 \| 0.2912 \| 0.2752 \| 0.4444 \| 0.3400 \| 0.8730 \| \| No log \| 2.0 \| 60 \| 0.2772 \| 0.4005 \| 0.4589 \| 0.4277 \| 0.8911 \| \| No log \| 3.0 \| 90 \| 0.2267 \| 0.3642 \| 0.5281 \| 0.4311 \| 0.9043 \| \| No log \| 4.0 \| 120 \| 0.2129 \| 0.3617 \| 0.5455 \| 0.4350 \| 0.9140 \| \| No log \| 5.0 \| 150 \| 0.2399 \| 0.3797 \| 0.5556 \| 0.4511 \| 0.9114 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Devid/DialoGPT-small-Miku	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	10	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_BERT_token_itr0_0.0001_editorials_01_03_2022-15_50_21 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_BERT_token_itr0_0.0001_editorials_01_03_2022-15_50_21 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1059 - Precision: 0.0637 - Recall: 0.0080 - F1: 0.0141 - Accuracy: 0.9707 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 15 \| 0.1103 \| 0.12 \| 0.0135 \| 0.0243 \| 0.9772 \| \| No log \| 2.0 \| 30 \| 0.0842 \| 0.12 \| 0.0135 \| 0.0243 \| 0.9772 \| \| No log \| 3.0 \| 45 \| 0.0767 \| 0.12 \| 0.0135 \| 0.0243 \| 0.9772 \| \| No log \| 4.0 \| 60 \| 0.0754 \| 0.12 \| 0.0135 \| 0.0243 \| 0.9772 \| \| No log \| 5.0 \| 75 \| 0.0735 \| 0.12 \| 0.0135 \| 0.0243 \| 0.9772 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Devmapall/paraphrase-quora	[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "T5ForConditionalGeneration" ], "model_type": "t5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": true, "length_penalty": 2, "max_length": 200, "min_length": 30, "no_repeat_ngram_size": 3, "num_beams": 4, "prefix": "summarize: " }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to German: " }, "translation_en_to_fr": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to French: " }, "translation_en_to_ro": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to Romanian: " } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1801 - Precision: 0.6153 - Recall: 0.7301 - F1: 0.6678 - Accuracy: 0.9346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 11 \| 0.2746 \| 0.4586 \| 0.5922 \| 0.5169 \| 0.9031 \| \| No log \| 2.0 \| 22 \| 0.2223 \| 0.5233 \| 0.6181 \| 0.5668 \| 0.9148 \| \| No log \| 3.0 \| 33 \| 0.2162 \| 0.5335 \| 0.6699 \| 0.5940 \| 0.9274 \| \| No log \| 4.0 \| 44 \| 0.2053 \| 0.5989 \| 0.7055 \| 0.6478 \| 0.9237 \| \| No log \| 5.0 \| 55 \| 0.2123 \| 0.5671 \| 0.7249 \| 0.6364 \| 0.9267 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Devrim/prism-default	[ "license:mit" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_BERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_47_14 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_BERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_47_14 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6542 - Precision: 0.0092 - Recall: 0.0403 - F1: 0.0150 - Accuracy: 0.7291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 10 \| 0.5856 \| 0.0012 \| 0.0125 \| 0.0022 \| 0.6950 \| \| No log \| 2.0 \| 20 \| 0.5933 \| 0.0 \| 0.0 \| 0.0 \| 0.7282 \| \| No log \| 3.0 \| 30 \| 0.5729 \| 0.0051 \| 0.025 \| 0.0085 \| 0.7155 \| \| No log \| 4.0 \| 40 \| 0.6178 \| 0.0029 \| 0.0125 \| 0.0047 \| 0.7143 \| \| No log \| 5.0 \| 50 \| 0.6707 \| 0.0110 \| 0.0375 \| 0.0170 \| 0.7178 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
DevsIA/Devs_IA	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_distilBERT_token_itr0_1e-05_all_01_03_2022-15_43_47 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_distilBERT_token_itr0_1e-05_all_01_03_2022-15_43_47 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3343 - Precision: 0.1651 - Recall: 0.3039 - F1: 0.2140 - Accuracy: 0.8493 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 30 \| 0.4801 \| 0.0352 \| 0.0591 \| 0.0441 \| 0.7521 \| \| No log \| 2.0 \| 60 \| 0.3795 \| 0.0355 \| 0.0795 \| 0.0491 \| 0.8020 \| \| No log \| 3.0 \| 90 \| 0.3359 \| 0.0591 \| 0.1294 \| 0.0812 \| 0.8334 \| \| No log \| 4.0 \| 120 \| 0.3205 \| 0.0785 \| 0.1534 \| 0.1039 \| 0.8486 \| \| No log \| 5.0 \| 150 \| 0.3144 \| 0.0853 \| 0.1571 \| 0.1105 \| 0.8516 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
DevsIA/imagenes	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1206 - Precision: 0.0637 - Recall: 0.0080 - F1: 0.0141 - Accuracy: 0.9707 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 15 \| 0.1222 \| 0.12 \| 0.0139 \| 0.0249 \| 0.9736 \| \| No log \| 2.0 \| 30 \| 0.1159 \| 0.12 \| 0.0139 \| 0.0249 \| 0.9736 \| \| No log \| 3.0 \| 45 \| 0.1082 \| 0.12 \| 0.0139 \| 0.0249 \| 0.9736 \| \| No log \| 4.0 \| 60 \| 0.1042 \| 0.12 \| 0.0139 \| 0.0249 \| 0.9736 \| \| No log \| 5.0 \| 75 \| 0.1029 \| 0.12 \| 0.0139 \| 0.0249 \| 0.9736 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
DewiBrynJones/wav2vec2-large-xlsr-welsh	[ "cy", "dataset:common_voice", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]	automatic-speech-recognition	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_distilBERT_token_itr0_1e-05_essays_01_03_2022-15_41_29 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_distilBERT_token_itr0_1e-05_essays_01_03_2022-15_41_29 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3097 - Precision: 0.2769 - Recall: 0.4391 - F1: 0.3396 - Accuracy: 0.8878 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 11 \| 0.4573 \| 0.0094 \| 0.0027 \| 0.0042 \| 0.7702 \| \| No log \| 2.0 \| 22 \| 0.3660 \| 0.1706 \| 0.3253 \| 0.2239 \| 0.8516 \| \| No log \| 3.0 \| 33 \| 0.3096 \| 0.2339 \| 0.408 \| 0.2974 \| 0.8827 \| \| No log \| 4.0 \| 44 \| 0.2868 \| 0.2963 \| 0.4693 \| 0.3633 \| 0.8928 \| \| No log \| 5.0 \| 55 \| 0.2798 \| 0.3141 \| 0.48 \| 0.3797 \| 0.8960 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
DheerajPranav/Dialo-GPT-Rick-bot	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_40_24 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_40_24 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5794 - Precision: 0.0094 - Recall: 0.0147 - F1: 0.0115 - Accuracy: 0.7156 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 10 \| 0.6319 \| 0.08 \| 0.0312 \| 0.0449 \| 0.6753 \| \| No log \| 2.0 \| 20 \| 0.6265 \| 0.0364 \| 0.0312 \| 0.0336 \| 0.6764 \| \| No log \| 3.0 \| 30 \| 0.6216 \| 0.0351 \| 0.0312 \| 0.0331 \| 0.6762 \| \| No log \| 4.0 \| 40 \| 0.6193 \| 0.0274 \| 0.0312 \| 0.0292 \| 0.6759 \| \| No log \| 5.0 \| 50 \| 0.6183 \| 0.0222 \| 0.0312 \| 0.0260 \| 0.6773 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Dhito/am	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	"2022-03-01T14:36:14Z"	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2876 - Precision: 0.2345 - Recall: 0.4281 - F1: 0.3030 - Accuracy: 0.8728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 30 \| 0.3907 \| 0.0433 \| 0.0824 \| 0.0568 \| 0.7626 \| \| No log \| 2.0 \| 60 \| 0.3046 \| 0.2302 \| 0.4095 \| 0.2947 \| 0.8598 \| \| No log \| 3.0 \| 90 \| 0.2945 \| 0.2084 \| 0.4095 \| 0.2762 \| 0.8668 \| \| No log \| 4.0 \| 120 \| 0.2687 \| 0.2847 \| 0.4607 \| 0.3519 \| 0.8761 \| \| No log \| 5.0 \| 150 \| 0.2643 \| 0.2779 \| 0.4444 \| 0.3420 \| 0.8788 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Dhruva/Interstellar	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-15_32_16 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-15_32_16 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2663 - Precision: 0.3644 - Recall: 0.4985 - F1: 0.4210 - Accuracy: 0.8997 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 11 \| 0.5174 \| 0.0120 \| 0.0061 \| 0.0081 \| 0.6997 \| \| No log \| 2.0 \| 22 \| 0.4029 \| 0.1145 \| 0.3098 \| 0.1672 \| 0.8265 \| \| No log \| 3.0 \| 33 \| 0.3604 \| 0.2539 \| 0.4448 \| 0.3233 \| 0.8632 \| \| No log \| 4.0 \| 44 \| 0.3449 \| 0.2992 \| 0.4755 \| 0.3673 \| 0.8704 \| \| No log \| 5.0 \| 55 \| 0.3403 \| 0.3340 \| 0.4816 \| 0.3945 \| 0.8760 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Dibyaranjan/nl_image_search	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-15_30_39 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # correct_twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-15_30_39 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6169 - Precision: 0.0031 - Recall: 0.0357 - F1: 0.0057 - Accuracy: 0.6464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 10 \| 0.6339 \| 0.0116 \| 0.0120 \| 0.0118 \| 0.6662 \| \| No log \| 2.0 \| 20 \| 0.6182 \| 0.0064 \| 0.0120 \| 0.0084 \| 0.6688 \| \| No log \| 3.0 \| 30 \| 0.6139 \| 0.0029 \| 0.0241 \| 0.0052 \| 0.6659 \| \| No log \| 4.0 \| 40 \| 0.6172 \| 0.0020 \| 0.0241 \| 0.0037 \| 0.6622 \| \| No log \| 5.0 \| 50 \| 0.6165 \| 0.0019 \| 0.0241 \| 0.0036 \| 0.6599 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
DicoTiar/wisdomfiy	[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]	fill-mask	{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2811 - Precision: 0.3231 - Recall: 0.5151 - F1: 0.3971 - Accuracy: 0.8913 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 30 \| 0.2881 \| 0.2089 \| 0.3621 \| 0.2650 \| 0.8715 \| \| No log \| 2.0 \| 60 \| 0.2500 \| 0.2619 \| 0.3842 \| 0.3115 \| 0.8845 \| \| No log \| 3.0 \| 90 \| 0.2571 \| 0.2327 \| 0.4338 \| 0.3030 \| 0.8809 \| \| No log \| 4.0 \| 120 \| 0.2479 \| 0.3051 \| 0.4761 \| 0.3719 \| 0.8949 \| \| No log \| 5.0 \| 150 \| 0.2783 \| 0.3287 \| 0.4761 \| 0.3889 \| 0.8936 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
DiegoBalam12/institute_classification	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_0.0001_essays_01_03_2022-15_18_35 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilBERT_token_itr0_0.0001_essays_01_03_2022-15_18_35 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1832 - Precision: 0.6138 - Recall: 0.7169 - F1: 0.6613 - Accuracy: 0.9332 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 11 \| 0.2740 \| 0.4554 \| 0.5460 \| 0.4966 \| 0.8943 \| \| No log \| 2.0 \| 22 \| 0.2189 \| 0.5470 \| 0.6558 \| 0.5965 \| 0.9193 \| \| No log \| 3.0 \| 33 \| 0.2039 \| 0.5256 \| 0.6706 \| 0.5893 \| 0.9198 \| \| No log \| 4.0 \| 44 \| 0.2097 \| 0.5401 \| 0.6795 \| 0.6018 \| 0.9237 \| \| No log \| 5.0 \| 55 \| 0.2255 \| 0.6117 \| 0.6825 \| 0.6452 \| 0.9223 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Digakive/Hsgshs	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5923 - Precision: 0.0039 - Recall: 0.0212 - F1: 0.0066 - Accuracy: 0.7084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 10 \| 0.6673 \| 0.0476 \| 0.0128 \| 0.0202 \| 0.6652 \| \| No log \| 2.0 \| 20 \| 0.6211 \| 0.0 \| 0.0 \| 0.0 \| 0.6707 \| \| No log \| 3.0 \| 30 \| 0.6880 \| 0.0038 \| 0.0128 \| 0.0058 \| 0.6703 \| \| No log \| 4.0 \| 40 \| 0.6566 \| 0.0030 \| 0.0128 \| 0.0049 \| 0.6690 \| \| No log \| 5.0 \| 50 \| 0.6036 \| 0.0 \| 0.0 \| 0.0 \| 0.6868 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Dilmk2/DialoGPT-small-harrypotter	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	13	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_1e-05_all_01_03_2022-15_14_04 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilBERT_token_itr0_1e-05_all_01_03_2022-15_14_04 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3121 - Precision: 0.1204 - Recall: 0.2430 - F1: 0.1611 - Accuracy: 0.8538 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 30 \| 0.4480 \| 0.0209 \| 0.0223 \| 0.0216 \| 0.7794 \| \| No log \| 2.0 \| 60 \| 0.3521 \| 0.0559 \| 0.1218 \| 0.0767 \| 0.8267 \| \| No log \| 3.0 \| 90 \| 0.3177 \| 0.1208 \| 0.2504 \| 0.1629 \| 0.8487 \| \| No log \| 4.0 \| 120 \| 0.3009 \| 0.1296 \| 0.2607 \| 0.1731 \| 0.8602 \| \| No log \| 5.0 \| 150 \| 0.2988 \| 0.1393 \| 0.2693 \| 0.1836 \| 0.8599 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
DimaOrekhov/transformer-method-name	[ "pytorch", "encoder-decoder", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "EncoderDecoderModel" ], "model_type": "encoder-decoder", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	8	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_1e-05_essays_01_03_2022-15_11_44 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilBERT_token_itr0_1e-05_essays_01_03_2022-15_11_44 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3082 - Precision: 0.2796 - Recall: 0.4373 - F1: 0.3411 - Accuracy: 0.8887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 11 \| 0.5018 \| 0.0192 \| 0.0060 \| 0.0091 \| 0.7370 \| \| No log \| 2.0 \| 22 \| 0.4066 \| 0.1541 \| 0.2814 \| 0.1992 \| 0.8340 \| \| No log \| 3.0 \| 33 \| 0.3525 \| 0.1768 \| 0.3234 \| 0.2286 \| 0.8612 \| \| No log \| 4.0 \| 44 \| 0.3250 \| 0.2171 \| 0.3503 \| 0.2680 \| 0.8766 \| \| No log \| 5.0 \| 55 \| 0.3160 \| 0.2353 \| 0.3713 \| 0.2880 \| 0.8801 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Dimedrolza/DialoGPT-small-cyberpunk	[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]	conversational	{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	9	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_10_39 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_10_39 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5867 - Precision: 0.0119 - Recall: 0.0116 - F1: 0.0118 - Accuracy: 0.6976 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 10 \| 0.5730 \| 0.0952 \| 0.0270 \| 0.0421 \| 0.7381 \| \| No log \| 2.0 \| 20 \| 0.5755 \| 0.0213 \| 0.0135 \| 0.0165 \| 0.7388 \| \| No log \| 3.0 \| 30 \| 0.5635 \| 0.0196 \| 0.0135 \| 0.016 \| 0.7416 \| \| No log \| 4.0 \| 40 \| 0.5549 \| 0.0392 \| 0.0270 \| 0.032 \| 0.7429 \| \| No log \| 5.0 \| 50 \| 0.5530 \| 0.0357 \| 0.0270 \| 0.0308 \| 0.7438 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Dmitry12/sber	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert_token_itr0_1e-05_all_01_03_2022-14_33_33 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert_token_itr0_1e-05_all_01_03_2022-14_33_33 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3255 - Precision: 0.1412 - Recall: 0.25 - F1: 0.1805 - Accuracy: 0.8491 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 30 \| 0.4549 \| 0.0228 \| 0.0351 \| 0.0276 \| 0.7734 \| \| No log \| 2.0 \| 60 \| 0.3577 \| 0.0814 \| 0.1260 \| 0.0989 \| 0.8355 \| \| No log \| 3.0 \| 90 \| 0.3116 \| 0.1534 \| 0.2648 \| 0.1943 \| 0.8611 \| \| No log \| 4.0 \| 120 \| 0.2975 \| 0.1792 \| 0.2967 \| 0.2234 \| 0.8690 \| \| No log \| 5.0 \| 150 \| 0.2935 \| 0.1873 \| 0.2998 \| 0.2305 \| 0.8715 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
DongHyoungLee/distilbert-base-uncased-finetuned-cola	[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]	text-classification	{ "architectures": [ "DistilBertForSequenceClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	27	"2022-02-27T18:11:24Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4064 - Accuracy: 0.8289 - F1: 0.8901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.4163 \| 0.8085 \| 0.8780 \| \| No log \| 2.0 \| 390 \| 0.4098 \| 0.8268 \| 0.8878 \| \| 0.312 \| 3.0 \| 585 \| 0.5892 \| 0.8244 \| 0.8861 \| \| 0.312 \| 4.0 \| 780 \| 0.7580 \| 0.8232 \| 0.8845 \| \| 0.312 \| 5.0 \| 975 \| 0.9028 \| 0.8183 \| 0.8824 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Donghyun/L2_BERT	[]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	0	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0926 - Accuracy: 0.9772 - F1: 0.9883 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 104 \| 0.0539 \| 0.9885 \| 0.9942 \| \| No log \| 2.0 \| 208 \| 0.0282 \| 0.9885 \| 0.9942 \| \| No log \| 3.0 \| 312 \| 0.0317 \| 0.9914 \| 0.9956 \| \| No log \| 4.0 \| 416 \| 0.0462 \| 0.9885 \| 0.9942 \| \| 0.0409 \| 5.0 \| 520 \| 0.0517 \| 0.9885 \| 0.9942 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Dongjae/mrc2reader	[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "XLMRobertaForQuestionAnswering" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3358 - Accuracy: 0.8688 - F1: 0.9225 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 81 \| 0.4116 \| 0.8382 \| 0.9027 \| \| No log \| 2.0 \| 162 \| 0.4360 \| 0.8382 \| 0.8952 \| \| No log \| 3.0 \| 243 \| 0.5719 \| 0.8382 \| 0.8995 \| \| No log \| 4.0 \| 324 \| 0.7251 \| 0.8493 \| 0.9021 \| \| No log \| 5.0 \| 405 \| 0.8384 \| 0.8456 \| 0.9019 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Dongmin/testmodel	[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "T5ForConditionalGeneration" ], "model_type": "t5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": true, "length_penalty": 2, "max_length": 200, "min_length": 30, "no_repeat_ngram_size": 3, "num_beams": 4, "prefix": "summarize: " }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to German: " }, "translation_en_to_fr": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to French: " }, "translation_en_to_ro": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to Romanian: " } } }	11	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5777 - Accuracy: 0.6794 - F1: 0.5010 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 48 \| 0.6059 \| 0.63 \| 0.4932 \| \| No log \| 2.0 \| 96 \| 0.6327 \| 0.705 \| 0.5630 \| \| No log \| 3.0 \| 144 \| 0.7003 \| 0.695 \| 0.5197 \| \| No log \| 4.0 \| 192 \| 0.9368 \| 0.69 \| 0.4655 \| \| No log \| 5.0 \| 240 \| 1.1935 \| 0.685 \| 0.4425 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Waynehillsdev/Wayne_NLP_mT5	[ "pytorch", "tensorboard", "mt5", "text2text-generation", "dataset:cnn_dailymail", "transformers", "generated_from_trainer", "autotrain_compatible" ]	text2text-generation	{ "architectures": [ "MT5ForConditionalGeneration" ], "model_type": "mt5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	11	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4787 - Accuracy: 0.8138 - F1: 0.8785 - Precision: 0.8489 - Recall: 0.9101 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 390 \| 0.4335 \| 0.7732 \| 0.8533 \| 0.8209 \| 0.8883 \| \| 0.5141 \| 2.0 \| 780 \| 0.4196 \| 0.8037 \| 0.8721 \| 0.8446 \| 0.9015 \| \| 0.3368 \| 3.0 \| 1170 \| 0.4519 \| 0.8098 \| 0.8779 \| 0.8386 \| 0.9212 \| \| 0.2677 \| 4.0 \| 1560 \| 0.4787 \| 0.8122 \| 0.8785 \| 0.8452 \| 0.9146 \| \| 0.2677 \| 5.0 \| 1950 \| 0.4912 \| 0.8146 \| 0.8794 \| 0.8510 \| 0.9097 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Waynehillsdev/wav2vec2-base-timit-demo-colab	[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0" ]	automatic-speech-recognition	{ "architectures": [ "Wav2Vec2ForCTC" ], "model_type": "wav2vec2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	5	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6168 - Accuracy: 0.8286 - F1: 0.8887 - Precision: 0.8628 - Recall: 0.9162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 390 \| 0.3890 \| 0.8110 \| 0.8749 \| 0.8631 \| 0.8871 \| \| 0.4535 \| 2.0 \| 780 \| 0.3921 \| 0.8439 \| 0.8984 \| 0.8721 \| 0.9264 \| \| 0.266 \| 3.0 \| 1170 \| 0.4454 \| 0.8415 \| 0.8947 \| 0.8860 \| 0.9034 \| \| 0.16 \| 4.0 \| 1560 \| 0.5610 \| 0.8427 \| 0.8957 \| 0.8850 \| 0.9067 \| \| 0.16 \| 5.0 \| 1950 \| 0.6180 \| 0.8488 \| 0.9010 \| 0.8799 \| 0.9231 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Doohae/p_encoder	[ "pytorch" ]	null	{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4345 - Accuracy: 0.8321 - F1: 0.8904 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3922 \| 0.8061 \| 0.8747 \| \| No log \| 2.0 \| 390 \| 0.3764 \| 0.8171 \| 0.8837 \| \| 0.4074 \| 3.0 \| 585 \| 0.3873 \| 0.8220 \| 0.8843 \| \| 0.4074 \| 4.0 \| 780 \| 0.4361 \| 0.8232 \| 0.8854 \| \| 0.4074 \| 5.0 \| 975 \| 0.4555 \| 0.8159 \| 0.8793 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
Doohae/roberta	[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]	question-answering	{ "architectures": [ "RobertaForQuestionAnswering" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	3	null	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4917 - Accuracy: 0.8231 - F1: 0.8833 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3883 \| 0.8146 \| 0.8833 \| \| No log \| 2.0 \| 390 \| 0.3607 \| 0.8390 \| 0.8964 \| \| 0.4085 \| 3.0 \| 585 \| 0.3812 \| 0.8488 \| 0.9042 \| \| 0.4085 \| 4.0 \| 780 \| 0.3977 \| 0.8549 \| 0.9077 \| \| 0.4085 \| 5.0 \| 975 \| 0.4233 \| 0.8573 \| 0.9092 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
albert-base-v1	[ "pytorch", "tf", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	38,156	"2022-02-27T17:07:05Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7600 - Accuracy: 0.8144 - F1: 0.8788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3514 \| 0.8427 \| 0.8979 \| \| No log \| 2.0 \| 390 \| 0.3853 \| 0.8293 \| 0.8936 \| \| 0.3147 \| 3.0 \| 585 \| 0.5494 \| 0.8268 \| 0.8868 \| \| 0.3147 \| 4.0 \| 780 \| 0.6235 \| 0.8427 \| 0.8995 \| \| 0.3147 \| 5.0 \| 975 \| 0.8302 \| 0.8378 \| 0.8965 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
albert-base-v2	[ "pytorch", "tf", "jax", "rust", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	4,785,283	"2022-02-26T03:09:07Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4676 - Accuracy: 0.8299 - F1: 0.8892 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.4087 \| 0.8073 \| 0.8754 \| \| No log \| 2.0 \| 390 \| 0.3952 \| 0.8159 \| 0.8803 \| \| 0.4084 \| 3.0 \| 585 \| 0.4183 \| 0.8195 \| 0.8831 \| \| 0.4084 \| 4.0 \| 780 \| 0.4596 \| 0.8280 \| 0.8867 \| \| 0.4084 \| 5.0 \| 975 \| 0.4919 \| 0.8280 \| 0.8873 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
albert-large-v1	[ "pytorch", "tf", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	687	"2022-02-27T16:39:04Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4095 - Accuracy: 0.8263 - F1: 0.8865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3685 \| 0.8293 \| 0.8911 \| \| No log \| 2.0 \| 390 \| 0.3495 \| 0.8415 \| 0.8992 \| \| 0.4065 \| 3.0 \| 585 \| 0.3744 \| 0.8463 \| 0.9014 \| \| 0.4065 \| 4.0 \| 780 \| 0.4260 \| 0.8427 \| 0.8980 \| \| 0.4065 \| 5.0 \| 975 \| 0.4548 \| 0.8366 \| 0.8940 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
albert-large-v2	[ "pytorch", "tf", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	26,792	"2022-02-27T17:56:36Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6049 - Accuracy: 0.6926 - F1: 0.4160 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 48 \| 0.5835 \| 0.71 \| 0.0333 \| \| No log \| 2.0 \| 96 \| 0.5718 \| 0.715 \| 0.3871 \| \| No log \| 3.0 \| 144 \| 0.5731 \| 0.715 \| 0.4 \| \| No log \| 4.0 \| 192 \| 0.6009 \| 0.705 \| 0.3516 \| \| No log \| 5.0 \| 240 \| 0.6122 \| 0.7 \| 0.4000 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
albert-xlarge-v1	[ "pytorch", "tf", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	341	"2022-02-27T17:35:08Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3962 - Accuracy: 0.8231 - F1: 0.8873 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3591 \| 0.8366 \| 0.8950 \| \| No log \| 2.0 \| 390 \| 0.3558 \| 0.8415 \| 0.9012 \| \| 0.3647 \| 3.0 \| 585 \| 0.4049 \| 0.8427 \| 0.8983 \| \| 0.3647 \| 4.0 \| 780 \| 0.5030 \| 0.8378 \| 0.8949 \| \| 0.3647 \| 5.0 \| 975 \| 0.5719 \| 0.8354 \| 0.8943 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
albert-xlarge-v2	[ "pytorch", "tf", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]	fill-mask	{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }	2,973	"2022-02-27T17:12:41Z"	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7600 - Accuracy: 0.8144 - F1: 0.8788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 195 \| 0.3514 \| 0.8427 \| 0.8979 \| \| No log \| 2.0 \| 390 \| 0.3853 \| 0.8293 \| 0.8936 \| \| 0.3147 \| 3.0 \| 585 \| 0.5494 \| 0.8268 \| 0.8868 \| \| 0.3147 \| 4.0 \| 780 \| 0.6235 \| 0.8427 \| 0.8995 \| \| 0.3147 \| 5.0 \| 975 \| 0.8302 \| 0.8378 \| 0.8965 \| ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

CAMeL-Lab/bert-base-arabic-camelbert-ca

[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

580

null

--- language: "en" tags: - English - Bible dataset: - English Bible Translation Dataset - Link: https://www.kaggle.com/oswinrh/bible inference: false --- ## Dataset English Bible Translation Dataset (https://www.kaggle.com/oswinrh/bible) *NOTE:* It is `roberta-base` fine-tuned (for MLM objective) for 1 epoch (using MLM objective) on the 7 `.csv` files mentioned above, which consist of around 5.5M words. ## Citation If you use this model in your work, please add the following citation - ``` @inproceedings{nandy-etal-2021-cs60075, title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora", author = "Nandy, Abhilash and Adak, Sayantan and Halder, Tanurima and Pokala, Sai Mahesh", booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.semeval-1.87", doi = "10.18653/v1/2021.semeval-1.87", pages = "678--682", abstract = "The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).", } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-poetry

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:1905.05700", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

37

null

--- language: - English tags: - EManuals - customer support - QA - bert --- Refer to https://aclanthology.org/2021.findings-emnlp.392/ for the paper and https://sites.google.com/view/emanualqa/home for the project website ## Citation Please cite the work if you would like to use it. ``` @inproceedings{nandy-etal-2021-question-answering, title = "Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework", author = "Nandy, Abhilash and Sharma, Soumya and Maddhashiya, Shubham and Sachdeva, Kapil and Goyal, Pawan and Ganguly, NIloy", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.392", doi = "10.18653/v1/2021.findings-emnlp.392", pages = "4600--4609", abstract = "Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40{\%} in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.", } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-pos-glf

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

{ "architectures": [ "BertForTokenClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

54

null

--- language: - English tags: - Europarl - roberta datasets: - Europarl --- Refer to https://aclanthology.org/2021.semeval-1.87/ ## Citation If you use this model in your work, please add the following citation - ``` @inproceedings{nandy-etal-2021-cs60075, title = "cs60075{\_}team2 at {S}em{E}val-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora", author = "Nandy, Abhilash and Adak, Sayantan and Halder, Tanurima and Pokala, Sai Mahesh", booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.semeval-1.87", doi = "10.18653/v1/2021.semeval-1.87", pages = "678--682", abstract = "The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We perform ablation studies on selecting the transformer models and how their individual complexity scores are aggregated to get the resulting complexity scores. Our method achieves a best Pearson Correlation of 0.784 in sub-task 1 (single word) and 0.836 in sub-task 2 (multiple word expressions).", } ```

CAMeL-Lab/bert-base-arabic-camelbert-da-sentiment

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "has_space" ]

text-classification

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19,850

null

--- language: en tags: - spanbert datasets: - ade_corpus_v2 widget: - text: "Having fever after taking paracetamol." example_title: "NER" - text: "Birth defects associated with thalidomide." example_title: "NER" - text: "Deafness and kidney failure associated with gentamicin (an antibiotic)." example_title: "NER" - text: "Bleeding of the intestine associated with aspirin therapy." example_title: "NER" --- spanbert-large-cased fine-tuned for "Adverse drug reaction" and "Drug" span Extraction. Details of spanbert-large-cased: https://huggingface.co/SpanBERT/spanbert-large-cased Details of the downstream task (Adverse drug reaction and Drug Extraction) - Dataset https://huggingface.co/datasets/ade_corpus_v2

CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]

text-classification

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45

null

# Dataset --- --- datasets: - covid_qa_deepset --- --- Covid 19 question answering data obtained from [covid_qa_deepset](https://huggingface.co/datasets/covid_qa_deepset). # Original Repository Repository for the fine tuning, inference and evaluation scripts can be found [here](https://github.com/abhijithneilabraham/Covid-QA). # Model in action ``` import torch from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("abhijithneilabraham/longformer_covid_qa") model = AutoModelForQuestionAnswering.from_pretrained("abhijithneilabraham/longformer_covid_qa") question = "In this way, what do the mRNA-destabilising RBPs constitute ?" text = """ In this way, mRNA-destabilising RBPs constitute a 'brake' on the immune system, which may ultimately be toggled therapeutically. I anticipate continued efforts in this area will lead to new methods of regaining control over inflammation in autoimmunity, selectively enhancing immunity in immunotherapy, and modulating RNA synthesis and virus replication during infection. Another mRNA under post-transcriptional regulation by Regnase-1 and Roquin is Furin, which encodes a conserved proprotein convertase crucial in human health and disease. Furin, along with other PCSK family members, is widely implicated in immune regulation, cancer and the entry, maturation or release of a broad array of evolutionarily diverse viruses including human papillomavirus (HPV), influenza (IAV), Ebola (EboV), dengue (DenV) and human immunodeficiency virus (HIV). Here, Braun and Sauter review the roles of furin in these processes, as well as the history and future of furin-targeting therapeutics. 7 They also discuss their recent work revealing how two IFN-cinducible factors exhibit broad-spectrum inhibition of IAV, measles (MV), zika (ZikV) and HIV by suppressing furin activity. 8 Over the coming decade, I expect to see an ever-finer spatiotemporal resolution of host-oriented therapies to achieve safe, effective and broad-spectrum yet costeffective therapies for clinical use. The increasing abundance of affordable, sensitive, high-throughput genome sequencing technologies has led to a recent boom in metagenomics and the cataloguing of the microbiome of our world. The MinION nanopore sequencer is one of the latest innovations in this space, enabling direct sequencing in a miniature form factor with only minimal sample preparation and a consumer-grade laptop computer. Nakagawa and colleagues here report on their latest experiments using this system, further improving its performance for use in resource-poor contexts for meningitis diagnoses. 9 While direct sequencing of viral genomic RNA is challenging, this system was recently used to directly sequence an RNA virus genome (IAV) for the first time. 10 I anticipate further improvements in the performance of such devices over the coming decade will transform virus surveillance efforts, the importance of which was underscored by the recent EboV and novel coronavirus (nCoV / COVID-19) outbreaks, enabling rapid deployment of antiviral treatments that take resistance-conferring mutations into account. Decades of basic immunology research have provided a near-complete picture of the main armaments in the human antiviral arsenal. Nevertheless, this focus on mammalian defences and pathologies has sidelined examination of the types and roles of viruses and antiviral defences that exist throughout our biosphere. One case in point is the CRISPR/Cas antiviral immune system of prokaryotes, which is now repurposed as a revolutionary gene-editing biotechnology in plants and animals. 11 Another is the ancient lineage of nucleocytosolic large DNA viruses (NCLDVs), which are emerging human pathogens that possess enormous genomes of up to several megabases in size encoding hundreds of proteins with unique and unknown functions. 12 Moreover, hundreds of human-and avian-infective viruses such as IAV strain H5N1 are known, but recent efforts indicate the true number may be in the millions and many harbour zoonotic potential. 13 It is increasingly clear that host-virus interactions have generated truly vast yet poorly understood and untapped biodiversity. Closing this Special Feature, Watanabe and Kawaoka elaborate on neo-virology, an emerging field engaged in cataloguing and characterising this biodiversity through a global consortium. 14 I predict these efforts will unlock a vast wealth of currently unexplored biodiversity, leading to biotechnologies and treatments that leverage the host-virus interactions developed throughout evolution. When biomedical innovations fall into the 'Valley of Death', patients who are therefore not reached all too often fall with them. Being entrusted with the resources and expectation to conceive, deliver and communicate dividends to society is both cherished and eagerly pursued at every stage of our careers. Nevertheless, the road to research translation is winding and is built on a foundation of basic research. Supporting industry-academia collaboration and nurturing talent and skills in the Indo-Pacific region are two of the four pillars of the National Innovation and Science Agenda. 2 These frame Australia's Medical Research and Innovation Priorities, which include antimicrobial resistance, global health and health security, drug repurposing and translational research infrastructure, 15 capturing many of the key elements of this CTI Special Feature. Establishing durable international relationships that integrate diverse expertise is essential to delivering these outcomes. To this end, NHMRC has recently taken steps under the International Engagement Strategy 16 to increase cooperation with its counterparts overseas. These include the Japan Agency for Medical Research and Development (AMED), tasked with translating the biomedical research output of that country. Given the reciprocal efforts at accelerating bilateral engagement currently underway, 17 the prospects for new areas of international cooperation and mobility have never been more exciting nor urgent. With the above in mind, all contributions to this CTI Special Feature I have selected from research presented by fellow invitees to the 2018 Awaji International Forum on Infection and Immunity (AIFII) and 2017 Consortium of Biological Sciences (ConBio) conferences in Japan. Both Australia and Japan have strong traditions in immunology and related disciplines, and I predict that the quantity, quality and importance of our bilateral cooperation will accelerate rapidly over the short to medium term. By expanding and cooperatively leveraging our respective research strengths, our efforts may yet solve the many pressing disease, cost and other sustainability issues of our time. """ encoding = tokenizer(question, text, return_tensors="pt") input_ids = encoding["input_ids"] # default is local attention everywhere # the forward method will automatically set global attention on question tokens attention_mask = encoding["attention_mask"] start_scores, end_scores = model(input_ids, attention_mask=attention_mask) all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist()) answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1] answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens)) # output => a 'brake' on the immune system ```

CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]

text-classification

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63

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--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased') model = AutoModel.from_pretrained('abhijithneilabraham/stsb_multi_mt_distilbert-base-uncased') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 25, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 900, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf

[ "pytorch", "tf", "bert", "token-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

token-classification

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132

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--- language: - en license: apache-2.0 datasets: - squad_v2 model-index: - name: abhilash1910/albert-squad-v2 results: - task: type: question-answering name: Question Answering dataset: name: squad_v2 type: squad_v2 config: squad_v2 split: validation metrics: - type: exact_match value: 23.6563 name: Exact Match verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTE5ZTM2YzIwZjBhYjM0ZDUyNzBiMjg1YjZhMGJiMGViMjYzYjQ5ZmI4MGFkYmU4YjY1OTNjYzAwZWRlZjIwNSIsInZlcnNpb24iOjF9.jlvV8WRPSPKJm6UdApoh-dXcAOmLPtF5smsHt39RoO4sFzzbH6elUz5yPF5Lt9Yc2YDIl6c8JDsODqMxmsD0Bg - type: f1 value: 29.3808 name: F1 verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiN2ZjYWRlYTI1NDkwYzNhMzM5YTg2NjZmODg0NjNkOGM3YjM2NTlkYjVhZWI0MzlmNjNkMTMxODlkNTY3ODBkMiIsInZlcnNpb24iOjF9.CR1MYeU3uqld9bbI8CLupMtote4WEG9fIq9enwhFJfVpChIT9BGKm86zaPmXHg0yBaNHgkMaEt_a-DaIdiEwAg ---

CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment

[ "pytorch", "tf", "bert", "text-classification", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0" ]

text-classification

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855

null

--- tags: - finance --- # Roberta Masked Language Model Trained On Financial Phrasebank Corpus This is a Masked Language Model trained with [Roberta](https://huggingface.co/transformers/model_doc/roberta.html) on a Financial Phrasebank Corpus. The model is built using Huggingface transformers. The model can be found at :[Financial_Roberta](https://huggingface.co/abhilash1910/financial_roberta) ## Specifications The corpus for training is taken from the Financial Phrasebank (Malo et al)[https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientations_in_Economic_Texts]. ## Model Specification The model chosen for training is [Roberta](https://arxiv.org/abs/1907.11692) with the following specifications: 1. vocab_size=56000 2. max_position_embeddings=514 3. num_attention_heads=12 4. num_hidden_layers=6 5. type_vocab_size=1 This is trained by using RobertaConfig from transformers package. The model is trained for 10 epochs with a gpu batch size of 64 units. ## Usage Specifications For using this model, we have to first import AutoTokenizer and AutoModelWithLMHead Modules from transformers After that we have to specify, the pre-trained model,which in this case is 'abhilash1910/financial_roberta' for the tokenizers and the model. ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("abhilash1910/financial_roberta") model = AutoModelWithLMHead.from_pretrained("abhilash1910/financial_roberta") ``` After this the model will be downloaded, it will take some time to download all the model files. For testing the model, we have to import pipeline module from transformers and create a masked output model for inference as follows: ```python from transformers import pipeline model_mask = pipeline('fill-mask', model='abhilash1910/inancial_roberta') model_mask("The company had a <mask> of 20% in 2020.") ``` Some of the examples are also provided with generic financial statements: Example 1: ```python model_mask("The company had a <mask> of 20% in 2020.") ``` Output: ```bash [{'sequence': '<s>The company had a profit of 20% in 2020.</s>', 'score': 0.023112965747714043, 'token': 421, 'token_str': 'Ġprofit'}, {'sequence': '<s>The company had a loss of 20% in 2020.</s>', 'score': 0.021379893645644188, 'token': 616, 'token_str': 'Ġloss'}, {'sequence': '<s>The company had a year of 20% in 2020.</s>', 'score': 0.0185744296759367, 'token': 443, 'token_str': 'Ġyear'}, {'sequence': '<s>The company had a sales of 20% in 2020.</s>', 'score': 0.018143286928534508, 'token': 428, 'token_str': 'Ġsales'}, {'sequence': '<s>The company had a value of 20% in 2020.</s>', 'score': 0.015319528989493847, 'token': 776, 'token_str': 'Ġvalue'}] ``` Example 2: ```python model_mask("The <mask> is listed under NYSE") ``` Output: ```bash [{'sequence': '<s>The company is listed under NYSE</s>', 'score': 0.1566661298274994, 'token': 359, 'token_str': 'Ġcompany'}, {'sequence': '<s>The total is listed under NYSE</s>', 'score': 0.05542507395148277, 'token': 522, 'token_str': 'Ġtotal'}, {'sequence': '<s>The value is listed under NYSE</s>', 'score': 0.04729423299431801, 'token': 776, 'token_str': 'Ġvalue'}, {'sequence': '<s>The order is listed under NYSE</s>', 'score': 0.02533523552119732, 'token': 798, 'token_str': 'Ġorder'}, {'sequence': '<s>The contract is listed under NYSE</s>', 'score': 0.02087237872183323, 'token': 635, 'token_str': 'Ġcontract'}] ``` ## Resources For all resources , please look into the [HuggingFace](https://huggingface.co/) Site and the [Repositories](https://github.com/huggingface).

CAMeL-Lab/bert-base-arabic-camelbert-msa-quarter

[ "pytorch", "tf", "jax", "bert", "fill-mask", "ar", "arxiv:2103.06678", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

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12

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--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - abhishek/autonlp-data-ferd1 --- # Model Trained Using AutoNLP - Problem type: Binary Classification - Model ID: 2652021 ## Validation Metrics - Loss: 0.3934604227542877 - Accuracy: 0.8411030860144452 - Precision: 0.8201550387596899 - Recall: 0.8076335877862595 - AUC: 0.8946767157983608 - F1: 0.8138461538461538 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-ferd1-2652021 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```

CLTL/icf-levels-att

[ "pytorch", "roberta", "text-classification", "nl", "transformers", "license:mit" ]

text-classification

{ "architectures": [ "RobertaForSequenceClassification" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

32

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Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on German using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz. As capitalization is an important part of the German language (eg. Sie vs. sie). I trained a model using a vocab that includes both lower case and upper case letters in hopes that the model would learn the correct casing. This removes the need to do any post-processing like truecasing. | Reference | Prediction | | ------------- | ------------- | | **Die** zoologische **Einordnung** der **Spezies** ist seit **Jahrzehnten** umstritten | **Die** psoologische **Einordnung** der **Spezies** ist seit **Jahrzehnten** umstritten | | **Hauptgeschäftsfeld** war ursprünglich der öffentliche **Sektor** in **Irland** | **Hauptgeschäftsfeld** war ursprünglich der öffentliche **Sektor** in **Irland** | | **Er** vertrat den **Wahlkreis Donauwörth** im **Parlament** | **Er** vertrat den **Wahlkreis DonauWört** im **Parlament** | | **Ich** bin gespannt welche **Lieder** sie wählt | **Ich** bin gespannt welche **Lieder** see wählt | | **Eine** allgemein verbindliche **Definition** gibt es nicht | **Eine** allgemeinverbindliche **Definition** gibt es nicht | ``` from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC import soundfile as sf import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("abnerh/wav2vec2-xlsr-300m-german-truecase") model = Wav2Vec2ForCTC.from_pretrained("abnerh/wav2vec2-xlsr-300m-german-truecase") speech, sr = sf.read('audio.wav') # tokenize input_values = processor(speech, return_tensors="pt", padding="longest").input_values # Batch size 1 # retrieve logits logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) # print transcription results print(transcription) ```

CLTL/icf-levels-fac

[ "pytorch", "roberta", "text-classification", "nl", "transformers", "license:mit" ]

text-classification

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32

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# Transferring Monolingual Model to Low-Resource Language: The Case Of Tigrinya: ## Proposed Method: <img src="data/proposed.png" height = "330" width ="760" > The proposed method transfers a mono-lingual Transformer model into new target language at lexical level by learning new token embeddings. All implementation in this repo uses XLNet as a source Transformer model, however, other Transformer models can also be used similarly. ## Main files: All files are IPython Notebook files which can be excuted simply in Google Colab. - train.ipynb : Fine-tunes XLNet (mono-lingual transformer) on new target language (Tigrinya) sentiment analysis dataset. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bSSrKE-TSphUyrNB2UWhFI-Bkoz0a5l0?usp=sharing) - test.ipynb : Evaluates the fine-tuned model on test data. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/17R1lvRjxILVNk971vzZT79o2OodwaNIX?usp=sharing) - token_embeddings.ipynb : Trains a word2vec token embeddings for Tigrinya language. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1hCtetAllAjBw28EVQkJFpiKdFtXmuxV7?usp=sharing) - process_Tigrinya_comments.ipynb : Extracts Tigrinya comments from mixed language contents. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1-ndLlBV-iLZNBW3Z8OfKAqUUCjvGbdZU?usp=sharing) - extract_YouTube_comments.ipynb : Downloads available comments from a YouTube channel ID. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1b7G85wHKe18y45JIDtvDJdO5dOkRmDdp?usp=sharing) - auto_labelling.ipynb : Automatically labels Tigrinya comments in to positive or negative sentiments based on [Emoji's sentiment](http://kt.ijs.si/data/Emoji_sentiment_ranking/). [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wnZf7CBBCIr966vRUITlxKCrANsMPpV7?usp=sharing) ## Tigrinya Tokenizer: A [sentencepiece](https://github.com/google/sentencepiece) based tokenizer for Tigrinya has been released to the public and can be accessed as in the following: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("abryee/TigXLNet") tokenizer.tokenize("ዋዋዋው እዛ ፍሊም ካብተን ዘድንቀን ሓንቲ ኢያ ሞ ብጣዕሚ ኢና ነመስግን ሓንቲ ክብላ ደልየ ዘሎኹ ሓደራኣኹም ኣብ ጊዜኹም ተረክቡ") ## TigXLNet: A new general purpose transformer model for low-resource language Tigrinya is also released to the public and be accessed as in the following: from transformers import AutoConfig, AutoModel config = AutoConfig.from_pretrained("abryee/TigXLNet") config.d_head = 64 model = AutoModel.from_pretrained("abryee/TigXLNet", config=config) ## Evaluation: The proposed method is evaluated using two datasets: - A newly created sentiment analysis dataset for low-resource language (Tigriyna). <table> <tr> <td> <table> <thead> <tr> <th>Models</th> <th>Configuration</th> <th>F1-Score</th> </tr> </thead> <tbody> <tr> <td rowspan=3>BERT</td> <td rowspan=1>+Frozen BERT weights</td> <td>54.91</td> </tr> <tr> <td rowspan=1>+Random embeddings</td> <td>74.26</td> </tr> <tr> <td rowspan=1>+Frozen token embeddings</td> <td>76.35</td> </tr> <tr> <td rowspan=3>mBERT</td> <td rowspan=1>+Frozen mBERT weights</td> <td>57.32</td> </tr> <tr> <td rowspan=1>+Random embeddings</td> <td>76.01</td> </tr> <tr> <td rowspan=1>+Frozen token embeddings</td> <td>77.51</td> </tr> <tr> <td rowspan=3>XLNet</td> <td rowspan=1>+Frozen XLNet weights</td> <td>68.14</td> </tr> <tr> <td rowspan=1>+Random embeddings</td> <td>77.83</td> </tr> <tr> <td rowspan=1>+Frozen token embeddings</td> <td>81.62</td> </tr> </tbody> </table> </td> <td><img src="data/effect_of_dataset_size.png" alt="3" width = 480px height = 280px></td> </tr> </table> - Cross-lingual Sentiment dataset ([CLS](https://zenodo.org/record/3251672#.Xs65VzozbIU)). <table> <thead> <tr> <th rowspan=2>Models</th> <th rowspan=1 colspan=3>English</th> <th rowspan=1 colspan=3>German</th> <th rowspan=1 colspan=3>French</th> <th rowspan=1 colspan=3>Japanese</th> <th rowspan=2>Average</th> </tr> <tr> <th colspan=1>Books</th> <th colspan=1>DVD</th> <th colspan=1>Music</th> <th colspan=1>Books</th> <th colspan=1>DVD</th> <th colspan=1>Music</th> <th colspan=1>Books</th> <th colspan=1>DVD</th> <th colspan=1>Music</th> <th colspan=1>Books</th> <th colspan=1>DVD</th> <th colspan=1>Music</th> </tr> </thead> <tbody> <tr> <td colspan=1>XLNet</td> <td colspan=1>92.90</td> <td colspan=1>93.31</td> <td colspan=1>92.02</td> <td colspan=1>85.23</td> <td colspan=1>83.30</td> <td colspan=1>83.89</td> <td colspan=1>73.05</td> <td colspan=1>69.80</td> <td colspan=1>70.12</td> <td colspan=1>83.20</td> <td colspan=1>86.07</td> <td colspan=1>85.24</td> <td colspan=1>83.08</td> </tr> <tr> <td colspan=1>mBERT</td> <td colspan=1>92.78</td> <td colspan=1>90.30</td> <td colspan=1>91.88</td> <td colspan=1>88.65</td> <td colspan=1>85.85</td> <td colspan=1>90.38</td> <td colspan=1>91.09</td> <td colspan=1>88.57</td> <td colspan=1>93.67</td> <td colspan=1>84.35</td> <td colspan=1>81.77</td> <td colspan=1>87.53</td> <td colspan=1>88.90</td> </tr> </tbody> </table> ## Dataset used for this paper: We have constructed new sentiment analysis dataset for Tigrinya language and it can be found in the zip file (Tigrinya Sentiment Analysis Dataset) ## Citing our paper: Our paper can be accessed from ArXiv [link](https://arxiv.org/pdf/2006.07698.pdf), and please consider citing our work. @misc{tela2020transferring, title={Transferring Monolingual Model to Low-Resource Language: The Case of Tigrinya}, author={Abrhalei Tela and Abraham Woubie and Ville Hautamaki}, year={2020}, eprint={2006.07698}, archivePrefix={arXiv}, primaryClass={cs.CL} } ## Any questions, comments, feedback is appreciated! And can be forwarded to the following email: [email protected]

CallumRai/HansardGPT2

[ "pytorch", "jax", "gpt2", "text-generation", "transformers" ]

text-generation

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": true, "max_length": 50 }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

14

null

ruGPT-3 fine-tuned on russian fanfiction about Bangatan Boys (BTS).

Cameron/BERT-eec-emotion

[ "pytorch", "jax", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

36

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https://www.guilded.gg/thisiscineplex/overview/news/XRz48Dr6 https://www.guilded.gg/FLIXmasGR/overview/news/7R0WorPy https://www.guilded.gg/FLIXmasGR/overview/news/NyE5BPmy https://www.guilded.gg/FLIXmasGR/overview/news/2l3Konal https://www.guilded.gg/FLIXmasGR/overview/news/AykDjvVR https://www.guilded.gg/FLIXmasGR/overview/news/16YOGQoR https://www.guilded.gg/FLIXmasGR/overview/news/KR2ngpXR https://www.guilded.gg/FLIXmasGR/overview/news/xypa2qZR https://www.guilded.gg/FLIXmasGR/overview/news/A6jZGQk6 https://www.guilded.gg/FLIXmasGR/overview/news/1ROQVMe6 https://www.guilded.gg/FLIXmasGR/overview/news/4yAW0Kvl https://www.guilded.gg/FLIXmasGR/overview/news/JlaoGQBy https://www.guilded.gg/FLIXmasGR/overview/news/YyrPnVEl https://www.guilded.gg/FLIXmasGR/overview/news/4lGz3aBR https://www.guilded.gg/FLIXmasGR/overview/news/16nKkj1y https://www.guilded.gg/FLIXmasGR/overview/news/X6QA0Ng6 https://www.guilded.gg/FLIXmasGR/overview/news/XRz4xGa6 https://www.guilded.gg/FLIXmasGR/overview/news/PlqV9826 https://www.guilded.gg/FLIXmasGR/overview/news/7R0WokWy https://www.guilded.gg/FLIXmasGR/overview/news/qlDvK4dy https://www.guilded.gg/FLIXmasGR/overview/news/2l3KopZl https://www.guilded.gg/FLIXmasGR/overview/news/16YOGj4R https://www.guilded.gg/FLIXmasGR/overview/news/4ldxGzQl

Cameron/BERT-jigsaw-identityhate

[ "pytorch", "jax", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

37

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# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). `BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_laptop") model = AutoModel.from_pretrained("activebus/BERT-DK_laptop") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```

Cameron/BERT-jigsaw-severetoxic

[ "pytorch", "jax", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

30

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# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp. ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_rest") model = AutoModel.from_pretrained("activebus/BERT-DK_rest") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```

Cameron/BERT-mdgender-convai-binary

[ "pytorch", "jax", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

33

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# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. `BERT-PT_*` addtionally uses SQuAD 1.1. ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_laptop") model = AutoModel.from_pretrained("activebus/BERT-PT_laptop") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```

Cameron/BERT-mdgender-convai-ternary

[ "pytorch", "jax", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

38

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# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp. `BERT-PT_*` addtionally uses SQuAD 1.1. ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_rest") model = AutoModel.from_pretrained("activebus/BERT-PT_rest") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```

Cameron/BERT-mdgender-wizard

[ "pytorch", "jax", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

30

null

# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. Please visit https://github.com/howardhsu/BERT-for-RRC-ABSA for details. `BERT-XD_Review` is a cross-domain (beyond just `laptop` and `restaurant`) language model, where each example is from a single product / restaurant with the same rating, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on `bert-base-uncased`. The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers). ## Model Description The original model is from `BERT-base-uncased`. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-XD_Review") model = AutoModel.from_pretrained("activebus/BERT-XD_Review") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) `BERT_Review` is expected to have similar performance on domain-specific tasks (such as aspect extraction) as `BERT-DK`, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words). ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```

Cameron/BERT-rtgender-opgender-annotations

[ "pytorch", "jax", "bert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "BertForSequenceClassification" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

33

null

# ReviewBERT BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. `BERT_Review` is cross-domain (beyond just `laptop` and `restaurant`) language model with one example from randomly mixed domains, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on `bert-base-uncased`. The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers). ## Model Description The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus. Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/). ## Instructions Loading the post-trained weights are as simple as, e.g., ```python import torch from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("activebus/BERT_Review") model = AutoModel.from_pretrained("activebus/BERT_Review") ``` ## Evaluation Results Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) `BERT_Review` is expected to have similar performance on domain-specific tasks (such as aspect extraction) as `BERT-DK`, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words). ## Citation If you find this work useful, please cite as following. ``` @inproceedings{xu_bert2019, title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis", author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.", booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics", month = "jun", year = "2019", } ```

Canadiancaleb/DialoGPT-small-walter

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

13

null

--- language: - pt --- This model was distilled from [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) ## Usage ```python from transformers import AutoTokenizer # Or BertTokenizer from transformers import AutoModelForPreTraining # Or BertForPreTraining for loading pretraining heads from transformers import AutoModel # or BertModel, for BERT without pretraining heads model = AutoModelForPreTraining.from_pretrained('adalbertojunior/distilbert-portuguese-cased') tokenizer = AutoTokenizer.from_pretrained('adalbertojunior/distilbert-portuguese-cased', do_lower_case=False) ``` You should fine tune it on your own data. It can achieve accuracy up to 99% relative to the original BERTimbau in some tasks.

Canadiancaleb/jessebot

[]

null

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0

null

--- language: - pt --- Image Captioning in Portuguese trained with ViT and GPT2 [DEMO](https://huggingface.co/spaces/adalbertojunior/image_captioning_portuguese) Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)

Captain272/lstm

[]

null

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0

null

This model has been trained by fine-tuning a BERTweet sentiment classification model named "finiteautomata/bertweet-base-sentiment-analysis", on a labeled positive/negative dataset of tweets. email : [email protected]

dccuchile/albert-base-spanish-finetuned-mldoc

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

{ "architectures": [ "AlbertForSequenceClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

34

null

--- title: Twitter Sentiments emoji: 😍 colorFrom: yellow colorTo: blue sdk: streamlit app_file: app.py pinned: false --- # Configuration `title`: _string_ Display title for the Space `emoji`: _string_ Space emoji (emoji-only character allowed) `colorFrom`: _string_ Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) `colorTo`: _string_ Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) `sdk`: _string_ Can be either `gradio` or `streamlit` `app_file`: _string_ Path to your main application file (which contains either `gradio` or `streamlit` Python code). Path is relative to the root of the repository. `pinned`: _boolean_ Whether the Space stays on top of your list.

dccuchile/albert-base-spanish-finetuned-pawsx

[ "pytorch", "albert", "text-classification", "transformers" ]

text-classification

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25

"2021-06-24T03:46:16Z"

--- license: apache-2.0 tags: - generated_from_trainer datasets: - null model_index: - name: 100perc results: - task: name: Causal Language Modeling type: text-generation ---  # 100perc This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4594 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | No log | 1.0 | 140 | 1.8292 | | No log | 2.0 | 280 | 1.7373 | | No log | 3.0 | 420 | 1.6889 | | 2.26 | 4.0 | 560 | 1.6515 | | 2.26 | 5.0 | 700 | 1.6258 | | 2.26 | 6.0 | 840 | 1.6063 | | 2.26 | 7.0 | 980 | 1.5873 | | 1.6847 | 8.0 | 1120 | 1.5749 | | 1.6847 | 9.0 | 1260 | 1.5634 | | 1.6847 | 10.0 | 1400 | 1.5513 | | 1.6073 | 11.0 | 1540 | 1.5421 | | 1.6073 | 12.0 | 1680 | 1.5352 | | 1.6073 | 13.0 | 1820 | 1.5270 | | 1.6073 | 14.0 | 1960 | 1.5203 | | 1.5545 | 15.0 | 2100 | 1.5142 | | 1.5545 | 16.0 | 2240 | 1.5089 | | 1.5545 | 17.0 | 2380 | 1.5048 | | 1.5156 | 18.0 | 2520 | 1.5009 | | 1.5156 | 19.0 | 2660 | 1.4970 | | 1.5156 | 20.0 | 2800 | 1.4935 | | 1.5156 | 21.0 | 2940 | 1.4897 | | 1.4835 | 22.0 | 3080 | 1.4865 | | 1.4835 | 23.0 | 3220 | 1.4851 | | 1.4835 | 24.0 | 3360 | 1.4820 | | 1.4565 | 25.0 | 3500 | 1.4787 | | 1.4565 | 26.0 | 3640 | 1.4774 | | 1.4565 | 27.0 | 3780 | 1.4749 | | 1.4565 | 28.0 | 3920 | 1.4748 | | 1.4326 | 29.0 | 4060 | 1.4728 | | 1.4326 | 30.0 | 4200 | 1.4692 | | 1.4326 | 31.0 | 4340 | 1.4692 | | 1.4326 | 32.0 | 4480 | 1.4668 | | 1.4126 | 33.0 | 4620 | 1.4664 | | 1.4126 | 34.0 | 4760 | 1.4659 | | 1.4126 | 35.0 | 4900 | 1.4643 | | 1.394 | 36.0 | 5040 | 1.4622 | | 1.394 | 37.0 | 5180 | 1.4629 | | 1.394 | 38.0 | 5320 | 1.4610 | | 1.394 | 39.0 | 5460 | 1.4623 | | 1.3775 | 40.0 | 5600 | 1.4599 | | 1.3775 | 41.0 | 5740 | 1.4600 | | 1.3775 | 42.0 | 5880 | 1.4580 | | 1.363 | 43.0 | 6020 | 1.4584 | | 1.363 | 44.0 | 6160 | 1.4577 | | 1.363 | 45.0 | 6300 | 1.4559 | | 1.363 | 46.0 | 6440 | 1.4545 | | 1.3484 | 47.0 | 6580 | 1.4568 | | 1.3484 | 48.0 | 6720 | 1.4579 | | 1.3484 | 49.0 | 6860 | 1.4562 | | 1.3379 | 50.0 | 7000 | 1.4558 | | 1.3379 | 51.0 | 7140 | 1.4556 | | 1.3379 | 52.0 | 7280 | 1.4581 | | 1.3379 | 53.0 | 7420 | 1.4554 | | 1.3258 | 54.0 | 7560 | 1.4561 | | 1.3258 | 55.0 | 7700 | 1.4553 | | 1.3258 | 56.0 | 7840 | 1.4555 | | 1.3258 | 57.0 | 7980 | 1.4572 | | 1.3158 | 58.0 | 8120 | 1.4551 | | 1.3158 | 59.0 | 8260 | 1.4573 | | 1.3158 | 60.0 | 8400 | 1.4561 | | 1.3072 | 61.0 | 8540 | 1.4557 | | 1.3072 | 62.0 | 8680 | 1.4548 | | 1.3072 | 63.0 | 8820 | 1.4547 | | 1.3072 | 64.0 | 8960 | 1.4556 | | 1.2986 | 65.0 | 9100 | 1.4555 | | 1.2986 | 66.0 | 9240 | 1.4566 | | 1.2986 | 67.0 | 9380 | 1.4558 | | 1.2916 | 68.0 | 9520 | 1.4565 | | 1.2916 | 69.0 | 9660 | 1.4552 | | 1.2916 | 70.0 | 9800 | 1.4558 | | 1.2916 | 71.0 | 9940 | 1.4553 | | 1.2846 | 72.0 | 10080 | 1.4579 | | 1.2846 | 73.0 | 10220 | 1.4572 | | 1.2846 | 74.0 | 10360 | 1.4572 | | 1.2792 | 75.0 | 10500 | 1.4564 | | 1.2792 | 76.0 | 10640 | 1.4576 | | 1.2792 | 77.0 | 10780 | 1.4571 | | 1.2792 | 78.0 | 10920 | 1.4580 | | 1.2736 | 79.0 | 11060 | 1.4578 | | 1.2736 | 80.0 | 11200 | 1.4583 | | 1.2736 | 81.0 | 11340 | 1.4576 | | 1.2736 | 82.0 | 11480 | 1.4580 | | 1.2699 | 83.0 | 11620 | 1.4575 | | 1.2699 | 84.0 | 11760 | 1.4583 | | 1.2699 | 85.0 | 11900 | 1.4588 | | 1.2664 | 86.0 | 12040 | 1.4590 | | 1.2664 | 87.0 | 12180 | 1.4593 | | 1.2664 | 88.0 | 12320 | 1.4582 | | 1.2664 | 89.0 | 12460 | 1.4591 | | 1.2627 | 90.0 | 12600 | 1.4595 | | 1.2627 | 91.0 | 12740 | 1.4585 | | 1.2627 | 92.0 | 12880 | 1.4590 | | 1.2613 | 93.0 | 13020 | 1.4590 | | 1.2613 | 94.0 | 13160 | 1.4598 | | 1.2613 | 95.0 | 13300 | 1.4592 | | 1.2613 | 96.0 | 13440 | 1.4597 | | 1.2591 | 97.0 | 13580 | 1.4593 | | 1.2591 | 98.0 | 13720 | 1.4593 | | 1.2591 | 99.0 | 13860 | 1.4597 | | 1.258 | 100.0 | 14000 | 1.4594 | ### Framework versions - Transformers 4.8.0 - Pytorch 1.8.1+cu111 - Datasets 1.8.0 - Tokenizers 0.10.3

dccuchile/albert-xlarge-spanish-finetuned-ner

[ "pytorch", "albert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

{ "architectures": [ "AlbertForTokenClassification" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

null

--- language: - zh_CN - zh_CN license: mit tags: - generated_from_trainer metrics: - rouge model_index: - name: filter-mlsum-pretrained results: - task: name: Translation type: translation metric: name: Rouge1 type: rouge value: 42.1802 ---  # filter-mlsum-pretrained This model is a fine-tuned version of [lincoln/mbart-mlsum-automatic-summarization](https://huggingface.co/lincoln/mbart-mlsum-automatic-summarization) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 2.1258 - Rouge1: 42.1802 - Rouge2: 28.8282 - Rougel: 38.353 - Rougelsum: 38.4497 - Gen Len: 15.7048 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Bleu | Gen Len | Validation Loss | Rouge-1 | Rouge-2 | Rouge-3 | Rouge-4 | |:-------------:|:-----:|:-----:|:------:|:-------:|:---------------:|:-------:|:-------:|:-------:|:-------:| | 3.2488 | 0.02 | 600 | 1.0077 | 16.5021 | 2.9137 | 0.3472 | 0.2187 | 0.129 | 0.0831 | | 2.8602 | 0.04 | 1200 | 1.0448 | 15.5959 | 2.7929 | 0.3555 | 0.231 | 0.1425 | 0.0948 | | 2.7612 | 0.06 | 1800 | 0.9912 | 15.9283 | 2.7275 | 0.3634 | 0.2327 | 0.139 | 0.0892 | | 2.71 | 0.08 | 2400 | 1.1238 | 16.029 | 2.6673 | 0.3705 | 0.2393 | 0.1448 | 0.0937 | | 2.6029 | 0.11 | 3000 | 1.091 | 15.8317 | 2.6153 | 0.3705 | 0.2382 | 0.1443 | 0.0943 | | 2.5834 | 0.13 | 3600 | 1.0894 | 15.9131 | 2.5937 | 0.3793 | 0.246 | 0.1517 | 0.1013 | | 2.5339 | 0.15 | 4200 | 1.1034 | 15.8331 | 2.5716 | 0.3758 | 0.2441 | 0.146 | 0.0948 | | 2.5176 | 0.17 | 4800 | 1.1365 | 16.2552 | 2.5338 | 0.3695 | 0.2385 | 0.1454 | 0.0942 | | 2.4962 | 0.19 | 5400 | 1.1237 | 16.0041 | 2.5145 | 0.3773 | 0.2462 | 0.1533 | 0.1017 | | 2.4573 | 0.21 | 6000 | 0.9416 | 16.1241 | 2.5056 | 0.3753 | 0.2457 | 0.1541 | 0.1012 | | 2.4324 | 0.23 | 6600 | 1.122 | 15.3448 | 2.4891 | 0.3824 | 0.2531 | 0.157 | 0.1033 | | 2.4343 | 0.25 | 7200 | 1.8299 | 15.5959 | 2.4728 | 0.384 | 0.2512 | 0.1556 | 0.1026 | | 2.4089 | 0.28 | 7800 | 1.7741 | 16.3421 | 2.4608 | 0.3818 | 0.2501 | 0.1556 | 0.102 | | 2.376 | 0.3 | 8400 | 1.1575 | 15.3834 | 2.4402 | 0.3887 | 0.2582 | 0.1611 | 0.1058 | | 2.3739 | 0.32 | 9000 | 1.7924 | 15.6455 | 2.4331 | 0.3902 | 0.2561 | 0.1587 | 0.1042 | | 2.3485 | 0.34 | 9600 | 2.2605 | 15.5407 | 2.4215 | 0.3712 | 0.2423 | 0.1493 | 0.0984 | | 2.3535 | 0.36 | 10200 | 1.2569 | 16.2538 | 2.4047 | 0.3837 | 0.2524 | 0.1572 | 0.1045 | | 2.3359 | 0.38 | 10800 | 1.2334 | 15.4607 | 2.4025 | 0.3808 | 0.2488 | 0.1531 | 0.0994 | | 2.3265 | 0.4 | 11400 | 1.116 | 16.2703 | 2.3926 | 0.3909 | 0.2574 | 0.159 | 0.1049 | | 2.3024 | 0.42 | 12000 | 1.0944 | 15.3807 | 2.3964 | 0.3883 | 0.2554 | 0.158 | 0.1043 | | 2.2988 | 0.45 | 12600 | 1.6318 | 15.5062 | 2.3616 | 0.3889 | 0.259 | 0.1617 | 0.107 | | 2.2966 | 0.47 | 13200 | 1.1887 | 15.8041 | 2.3728 | 0.3835 | 0.2556 | 0.1633 | 0.1111 | | 2.2823 | 0.49 | 13800 | 1.1252 | 15.9972 | 2.3591 | 0.3805 | 0.249 | 0.1571 | 0.1052 | | 2.2748 | 0.51 | 14400 | 1.0418 | 15.3021 | 2.3619 | 0.3862 | 0.2569 | 0.161 | 0.1072 | | 2.2624 | 0.53 | 15000 | 1.0299 | 15.8634 | 2.3415 | 0.3909 | 0.2575 | 0.1608 | 0.1072 | | 2.2585 | 0.55 | 15600 | 1.0671 | 15.5503 | 2.3557 | 0.3899 | 0.2586 | 0.1622 | 0.1077 | | 2.2586 | 0.57 | 16200 | 1.6521 | 15.4345 | 2.3431 | 0.389 | 0.2593 | 0.1642 | 0.1105 | | 2.2464 | 0.59 | 16800 | 1.2836 | 15.6124 | 2.3609 | 0.3934 | 0.2591 | 0.1593 | 0.1041 | | 2.2523 | 0.62 | 17400 | 1.7653 | 15.8648 | 2.3339 | 0.3958 | 0.2653 | 0.1683 | 0.1133 | | 2.2287 | 0.64 | 18000 | 1.3186 | 16.4455 | 2.3188 | 0.3911 | 0.2617 | 0.1678 | 0.1143 | | 2.2068 | 0.66 | 18600 | 1.6488 | 15.9062 | 2.3109 | 0.3919 | 0.262 | 0.1657 | 0.1115 | | 2.2195 | 0.68 | 19200 | 1.8291 | 15.5269 | 2.3271 | 0.3859 | 0.2575 | 0.1631 | 0.1081 | | 2.2128 | 0.7 | 19800 | 2.2759 | 15.8703 | 2.3113 | 0.3962 | 0.2655 | 0.1691 | 0.1123 | | 2.2071 | 0.72 | 20400 | 2.4205 | 15.9738 | 2.3036 | 0.3907 | 0.2608 | 0.1637 | 0.1081 | | 2.1975 | 0.74 | 21000 | 1.9886 | 15.8234 | 2.2919 | 0.3906 | 0.2632 | 0.169 | 0.1157 | | 2.1965 | 0.76 | 21600 | 1.8754 | 15.3434 | 2.2957 | 0.39 | 0.2608 | 0.1665 | 0.1114 | | 2.1886 | 0.78 | 22200 | 1.5683 | 15.3407 | 2.2835 | 0.3968 | 0.2658 | 0.168 | 0.1117 | | 2.185 | 0.81 | 22800 | 2.127 | 16.0566 | 2.2685 | 0.3913 | 0.2624 | 0.1691 | 0.114 | | 2.1697 | 0.83 | 23400 | 1.2554 | 15.7021 | 2.2888 | 0.3983 | 0.2676 | 0.1704 | 0.1148 | | 2.1637 | 0.85 | 24000 | 2.0099 | 16.2607 | 2.2767 | 0.3979 | 0.2681 | 0.1719 | 0.1181 | | 2.1559 | 0.87 | 24600 | 2.2632 | 15.2179 | 2.2840 | 0.3996 | 0.269 | 0.1714 | 0.1152 | | 2.1666 | 0.89 | 25200 | 1.2354 | 15.6828 | 2.2744 | 0.397 | 0.2655 | 0.1677 | 0.1108 | | 2.1388 | 0.91 | 25800 | 1.2576 | 15.7959 | 2.2661 | 0.3982 | 0.2655 | 0.1687 | 0.1128 | | 2.1458 | 0.93 | 26400 | 1.334 | 15.6428 | 2.2582 | 0.3976 | 0.2682 | 0.1711 | 0.1142 | | 2.1337 | 0.95 | 27000 | 1.287 | 16.1379 | 2.2474 | 0.4001 | 0.2654 | 0.1682 | 0.1119 | | 2.1324 | 0.98 | 27600 | 1.1739 | 16.0552 | 2.2487 | 0.4003 | 0.2664 | 0.168 | 0.1113 | | 2.1318 | 1.0 | 28200 | 2.1267 | 15.931 | 2.2553 | 0.4037 | 0.27 | 0.1714 | 0.1163 | | 2.0379 | 1.02 | 28800 | 1.1489 | 15.3421 | 2.2787 | 0.3962 | 0.263 | 0.1674 | 0.114 | | 1.9044 | 1.04 | 29400 | 1.6737 | 15.6 | 2.2538 | 0.4003 | 0.2693 | 0.1729 | 0.1161 | | 1.9149 | 1.06 | 30000 | 1.1077 | 15.771 | 2.2487 | 0.4062 | 0.274 | 0.1774 | 0.1209 | | 1.9211 | 1.08 | 30600 | 1.2744 | 15.0566 | 2.2708 | 0.4075 | 0.2742 | 0.1744 | 0.1172 | | 1.9285 | 1.1 | 31200 | 1.1875 | 16.1021 | 2.2443 | 0.3983 | 0.2652 | 0.1671 | 0.1124 | | 1.9106 | 1.12 | 31800 | 1.2422 | 15.36 | 2.2562 | 0.4079 | 0.2751 | 0.1762 | 0.119 | | 1.9313 | 1.15 | 32400 | 1.3036 | 15.8317 | 2.2515 | 0.4027 | 0.2717 | 0.1748 | 0.1196 | | 1.931 | 1.17 | 33000 | 1.138 | 16.1917 | 2.2415 | 0.4016 | 0.2701 | 0.1724 | 0.1179 | | 1.9232 | 1.19 | 33600 | 1.2741 | 15.6814 | 2.2511 | 0.4074 | 0.2757 | 0.1782 | 0.1222 | | 1.9233 | 1.21 | 34200 | 1.4101 | 15.8345 | 2.2388 | 0.4027 | 0.2712 | 0.1727 | 0.1174 | | 1.9172 | 1.23 | 34800 | 1.252 | 15.6124 | 2.2434 | 0.4046 | 0.2747 | 0.1783 | 0.1215 | | 1.9258 | 1.25 | 35400 | 1.2459 | 15.5062 | 2.2342 | 0.4107 | 0.2801 | 0.1814 | 0.1236 | | 1.9184 | 1.27 | 36000 | 1.2943 | 15.6083 | 2.2393 | 0.4119 | 0.2817 | 0.1839 | 0.1244 | | 1.9195 | 1.29 | 36600 | 1.1197 | 15.8359 | 2.2237 | 0.4014 | 0.2695 | 0.1699 | 0.1132 | | 1.932 | 1.31 | 37200 | 1.2212 | 15.9752 | 2.2202 | 0.4027 | 0.2708 | 0.1723 | 0.1168 | | 1.9161 | 1.34 | 37800 | 1.2541 | 15.5779 | 2.2236 | 0.4091 | 0.2783 | 0.1804 | 0.1244 | | 1.9115 | 1.36 | 38400 | 1.4237 | 15.8276 | 2.1993 | 0.4122 | 0.2813 | 0.1832 | 0.1258 | | 1.9108 | 1.38 | 39000 | 1.8321 | 16.0386 | 2.2079 | 0.412 | 0.2794 | 0.1806 | 0.1226 | | 1.921 | 1.4 | 39600 | 1.8388 | 15.5076 | 2.2158 | 0.411 | 0.2799 | 0.1804 | 0.1226 | | 1.9124 | 1.42 | 40200 | 1.915 | 16.0 | 2.2071 | 0.4032 | 0.2726 | 0.1742 | 0.1185 | | 1.9134 | 1.44 | 40800 | 2.1237 | 16.0372 | 2.1980 | 0.4036 | 0.2702 | 0.1689 | 0.1122 | | 1.9124 | 1.46 | 41400 | 2.4274 | 15.3421 | 2.2111 | 0.4037 | 0.274 | 0.1754 | 0.1203 | | 1.9149 | 1.48 | 42000 | 1.8393 | 15.5683 | 2.2105 | 0.4057 | 0.2748 | 0.1762 | 0.119 | | 1.9147 | 1.51 | 42600 | 1.2703 | 16.3048 | 2.1874 | 0.4084 | 0.2767 | 0.179 | 0.1233 | | 1.9075 | 1.53 | 43200 | 1.7775 | 15.9545 | 2.1946 | 0.4109 | 0.2807 | 0.1857 | 0.1286 | | 1.8996 | 1.55 | 43800 | 1.2485 | 15.6648 | 2.1924 | 0.4082 | 0.2749 | 0.1764 | 0.1196 | | 1.9003 | 1.57 | 44400 | 1.1624 | 15.8041 | 2.1895 | 0.4093 | 0.2758 | 0.1766 | 0.1194 | | 1.9048 | 1.59 | 45000 | 1.8167 | 16.2938 | 2.1843 | 0.407 | 0.2741 | 0.1779 | 0.1203 | | 1.9017 | 1.61 | 45600 | 2.0689 | 15.3931 | 2.2073 | 0.4111 | 0.2795 | 0.1811 | 0.1246 | | 1.8946 | 1.63 | 46200 | 1.7099 | 15.9917 | 2.1839 | 0.4095 | 0.2797 | 0.1826 | 0.1258 | | 1.886 | 1.65 | 46800 | 1.8287 | 15.8276 | 2.1945 | 0.4051 | 0.2761 | 0.1799 | 0.1237 | | 1.9068 | 1.68 | 47400 | 1.9476 | 15.3503 | 2.1926 | 0.4132 | 0.2819 | 0.1836 | 0.1262 | | 1.9008 | 1.7 | 48000 | 1.3086 | 15.5931 | 2.1857 | 0.4167 | 0.2868 | 0.1893 | 0.1303 | | 1.8965 | 1.72 | 48600 | 2.1687 | 15.8317 | 2.1781 | 0.402 | 0.2715 | 0.175 | 0.1197 | | 1.8907 | 1.74 | 49200 | 2.3316 | 15.8952 | 2.1661 | 0.4035 | 0.2717 | 0.1746 | 0.1193 | | 1.8938 | 1.76 | 49800 | 1.6839 | 15.6028 | 2.1736 | 0.4008 | 0.2693 | 0.1741 | 0.1184 | | 1.8769 | 1.78 | 50400 | 1.1867 | 15.9393 | 2.1723 | 0.403 | 0.272 | 0.1761 | 0.1201 | | 1.8813 | 1.8 | 51000 | 1.8509 | 16.2538 | 2.1454 | 0.4085 | 0.2773 | 0.1801 | 0.1227 | | 1.8913 | 1.82 | 51600 | 1.9677 | 15.7503 | 2.1691 | 0.4052 | 0.2786 | 0.1836 | 0.1274 | | 1.8785 | 1.85 | 52200 | 1.7 | 15.7559 | 2.1683 | 0.4132 | 0.2793 | 0.1796 | 0.1216 | | 1.881 | 1.87 | 52800 | 1.2867 | 16.0345 | 2.1372 | 0.416 | 0.2824 | 0.1837 | 0.1264 | | 1.8833 | 1.89 | 53400 | 1.761 | 16.0966 | 2.1501 | 0.4126 | 0.2808 | 0.1825 | 0.1253 | | 1.8727 | 1.91 | 54000 | 1.9868 | 15.8221 | 2.1504 | 0.4165 | 0.2828 | 0.1826 | 0.1233 | | 1.8901 | 1.93 | 54600 | 1.801 | 14.9393 | 2.2104 | 0.4151 | 0.2846 | 0.1848 | 0.1258 | | 1.8802 | 1.95 | 55200 | 2.0887 | 15.8069 | 2.1555 | 0.407 | 0.2766 | 0.1794 | 0.1214 | | 1.8827 | 1.97 | 55800 | 1.8323 | 15.8524 | 2.1510 | 0.4221 | 0.291 | 0.193 | 0.135 | | 1.8673 | 1.99 | 56400 | 1.2667 | 15.4262 | 2.1620 | 0.4092 | 0.2795 | 0.1836 | 0.1275 | | 1.6735 | 2.01 | 57000 | 1.821 | 15.8538 | 2.1836 | 0.4193 | 0.2875 | 0.189 | 0.1317 | | 1.6367 | 2.04 | 57600 | 2.5547 | 15.8055 | 2.1941 | 0.415 | 0.2831 | 0.1849 | 0.1284 | | 1.6326 | 2.06 | 58200 | 2.0999 | 15.9352 | 2.1743 | 0.4157 | 0.2829 | 0.1842 | 0.1267 | | 1.6354 | 2.08 | 58800 | 2.3907 | 15.68 | 2.1879 | 0.4233 | 0.2921 | 0.1936 | 0.1361 | | 1.6352 | 2.1 | 59400 | 1.979 | 16.1807 | 2.1735 | 0.4236 | 0.2907 | 0.193 | 0.1346 | | 1.6428 | 2.12 | 60000 | 2.2266 | 15.8759 | 2.1858 | 0.4204 | 0.2881 | 0.1896 | 0.1308 | | 1.6483 | 2.14 | 60600 | 1.9294 | 15.8469 | 2.1878 | 0.4237 | 0.2892 | 0.1901 | 0.1317 | | 1.6502 | 2.16 | 61200 | 1.7967 | 15.7131 | 2.1814 | 0.4164 | 0.2835 | 0.1852 | 0.1275 | | 1.6585 | 2.18 | 61800 | 1.1843 | 16.0579 | 2.1620 | 0.413 | 0.2828 | 0.1852 | 0.128 | | 1.6457 | 2.21 | 62400 | 1.7951 | 15.9862 | 2.1873 | 0.4194 | 0.2885 | 0.1908 | 0.1341 | | 1.6433 | 2.23 | 63000 | 1.6297 | 16.1324 | 2.1770 | 0.4039 | 0.2741 | 0.1773 | 0.1209 | | 1.6493 | 2.25 | 63600 | 1.8762 | 15.5131 | 2.1702 | 0.414 | 0.2851 | 0.1883 | 0.1292 | | 1.672 | 2.27 | 64200 | 2.1811 | 16.1945 | 2.1433 | 0.4198 | 0.2852 | 0.1854 | 0.1272 | | 1.6411 | 2.29 | 64800 | 2.0637 | 16.1434 | 2.1661 | 0.4103 | 0.2809 | 0.1848 | 0.1275 | | 1.6561 | 2.31 | 65400 | 2.452 | 15.5724 | 2.1761 | 0.4204 | 0.292 | 0.1935 | 0.135 | | 1.6516 | 2.33 | 66000 | 2.216 | 15.7048 | 2.1836 | 0.4186 | 0.2887 | 0.1909 | 0.1326 | | 1.6738 | 2.35 | 66600 | 1.7496 | 15.731 | 2.1452 | 0.4186 | 0.2904 | 0.1944 | 0.1364 | | 1.672 | 2.38 | 67200 | 1.3179 | 15.7697 | 2.1412 | 0.4206 | 0.2898 | 0.1936 | 0.1358 | | 1.6625 | 2.4 | 67800 | 2.3606 | 15.76 | 2.1412 | 0.4134 | 0.285 | 0.189 | 0.1315 | | 1.6725 | 2.42 | 68400 | 2.3687 | 15.4745 | 2.1825 | 0.4165 | 0.2874 | 0.1883 | 0.1303 | | 1.6588 | 2.44 | 69000 | 2.2056 | 15.8841 | 2.1307 | 0.4259 | 0.2952 | 0.1974 | 0.139 | | 1.6629 | 2.46 | 69600 | 1.7605 | 15.469 | 2.1523 | 0.4149 | 0.2861 | 0.1901 | 0.1327 | | 1.6716 | 2.48 | 70200 | 1.3733 | 15.3683 | 2.1546 | 0.4202 | 0.2889 | 0.1897 | 0.1314 | | 1.6708 | 2.5 | 70800 | 2.6313 | 15.7214 | 2.1408 | 0.4236 | 0.2937 | 0.1972 | 0.1395 | | 1.6637 | 2.52 | 71400 | 2.5112 | 15.909 | 2.1252 | 0.4203 | 0.2903 | 0.1935 | 0.1361 | | 1.6743 | 2.55 | 72000 | 2.2902 | 15.749 | 2.1326 | 0.426 | 0.297 | 0.1989 | 0.1404 | | 1.6681 | 2.57 | 72600 | 2.1003 | 16.3338 | 2.1120 | 0.4185 | 0.2876 | 0.1904 | 0.1342 | | 1.6791 | 2.59 | 73200 | 1.7082 | 15.7283 | 2.1269 | 0.4268 | 0.2968 | 0.1988 | 0.1392 | | 1.6643 | 2.61 | 73800 | 1.9914 | 16.0552 | 2.1166 | 0.4177 | 0.2886 | 0.1939 | 0.1369 | | 1.6666 | 2.63 | 74400 | 1.8012 | 16.0276 | 2.1242 | 0.4174 | 0.2875 | 0.19 | 0.1328 | | 1.67 | 2.65 | 75000 | 1.696 | 15.5559 | 2.1619 | 0.4196 | 0.2919 | 0.1939 | 0.136 | | 1.6794 | 2.67 | 75600 | 2.0322 | 15.6221 | 2.1425 | 0.4166 | 0.2871 | 0.1891 | 0.1328 | | 1.6753 | 2.69 | 76200 | 2.5736 | 15.7407 | 2.1432 | 0.4215 | 0.2928 | 0.1958 | 0.1388 | | 1.6807 | 2.71 | 76800 | 2.3404 | 15.7186 | 2.1240 | 0.4181 | 0.2885 | 0.1917 | 0.1346 | | 1.6707 | 2.74 | 77400 | 2.4439 | 15.5724 | 2.1246 | 0.4191 | 0.2906 | 0.1936 | 0.1359 | | 1.6736 | 2.76 | 78000 | 2.0595 | 16.2731 | 2.1053 | 0.4158 | 0.2869 | 0.1902 | 0.1324 | | 1.6651 | 2.78 | 78600 | 1.6489 | 15.6772 | 2.1365 | 0.4242 | 0.2924 | 0.1938 | 0.1346 | | 1.6746 | 2.8 | 79200 | 1.1565 | 15.9062 | 2.1232 | 0.4161 | 0.2848 | 0.1872 | 0.1308 | | 1.6666 | 2.82 | 79800 | 1.7445 | 15.9407 | 2.1417 | 0.414 | 0.2807 | 0.1817 | 0.1249 | | 1.6687 | 2.84 | 80400 | 1.9425 | 15.8676 | 2.1240 | 0.4088 | 0.2786 | 0.1821 | 0.1269 | | 1.6678 | 2.86 | 81000 | 1.6419 | 15.9214 | 2.1125 | 0.417 | 0.2873 | 0.188 | 0.1303 | | 1.6609 | 2.88 | 81600 | 1.8123 | 15.8579 | 2.1227 | 0.4199 | 0.2904 | 0.1916 | 0.1323 | ### Framework versions - Transformers 4.8.2 - Pytorch 1.8.1+cu111 - Datasets 1.9.0 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-mldoc

[ "pytorch", "bert", "text-classification", "transformers" ]

text-classification

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39

"2021-07-07T18:25:16Z"

--- language: - zh_CN - zh_CN license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model_index: - name: tmp results: - task: name: Translation type: translation metric: name: Bleu type: bleu value: 0.0099 ---  # tmp This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: nan - Bleu: 0.0099 - Gen Len: 3.3917 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1024 - eval_batch_size: 1024 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 2048 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 1 | nan | 0.0114 | 3.3338 | | No log | 2.0 | 2 | nan | 0.0114 | 3.3338 | | No log | 3.0 | 3 | nan | 0.0114 | 3.3338 | | No log | 4.0 | 4 | nan | 0.0114 | 3.3338 | | No log | 5.0 | 5 | nan | 0.0114 | 3.3338 | | No log | 6.0 | 6 | nan | 0.0114 | 3.3338 | | No log | 7.0 | 7 | nan | 0.0114 | 3.3338 | | No log | 8.0 | 8 | nan | 0.0114 | 3.3338 | | No log | 9.0 | 9 | nan | 0.0114 | 3.3338 | | No log | 10.0 | 10 | nan | 0.0114 | 3.3338 | | No log | 11.0 | 11 | nan | 0.0114 | 3.3338 | | No log | 12.0 | 12 | nan | 0.0114 | 3.3338 | | No log | 13.0 | 13 | nan | 0.0114 | 3.3338 | | No log | 14.0 | 14 | nan | 0.0114 | 3.3338 | | No log | 15.0 | 15 | nan | 0.0114 | 3.3338 | | No log | 16.0 | 16 | nan | 0.0114 | 3.3338 | | No log | 17.0 | 17 | nan | 0.0114 | 3.3338 | | No log | 18.0 | 18 | nan | 0.0114 | 3.3338 | | No log | 19.0 | 19 | nan | 0.0114 | 3.3338 | | No log | 20.0 | 20 | nan | 0.0114 | 3.3338 | ### Framework versions - Transformers 4.8.2 - Pytorch 1.8.1+cu111 - Datasets 1.9.0 - Tokenizers 0.10.3

dccuchile/bert-base-spanish-wwm-uncased-finetuned-ner

[ "pytorch", "bert", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

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5

"2021-07-02T15:01:28Z"

--- license: mit tags: - generated_from_trainer datasets: - null model_index: - name: topicalchat-multiturn results: - task: name: Causal Language Modeling type: text-generation ---  # topicalchat-multiturn This model is a fine-tuned version of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.5260 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 73 | 4.2992 | | No log | 2.0 | 146 | 3.4433 | | No log | 3.0 | 219 | 3.1606 | | No log | 4.0 | 292 | 3.0366 | | No log | 5.0 | 365 | 2.9679 | | No log | 6.0 | 438 | 2.9131 | | 4.1401 | 7.0 | 511 | 2.8752 | | 4.1401 | 8.0 | 584 | 2.8391 | | 4.1401 | 9.0 | 657 | 2.8118 | | 4.1401 | 10.0 | 730 | 2.7871 | | 4.1401 | 11.0 | 803 | 2.7659 | | 4.1401 | 12.0 | 876 | 2.7489 | | 4.1401 | 13.0 | 949 | 2.7331 | | 2.9768 | 14.0 | 1022 | 2.7196 | | 2.9768 | 15.0 | 1095 | 2.7071 | | 2.9768 | 16.0 | 1168 | 2.6940 | | 2.9768 | 17.0 | 1241 | 2.6854 | | 2.9768 | 18.0 | 1314 | 2.6728 | | 2.9768 | 19.0 | 1387 | 2.6647 | | 2.9768 | 20.0 | 1460 | 2.6562 | | 2.7864 | 21.0 | 1533 | 2.6482 | | 2.7864 | 22.0 | 1606 | 2.6439 | | 2.7864 | 23.0 | 1679 | 2.6326 | | 2.7864 | 24.0 | 1752 | 2.6107 | | 2.7864 | 25.0 | 1825 | 2.6043 | | 2.7864 | 26.0 | 1898 | 2.5970 | | 2.7864 | 27.0 | 1971 | 2.5908 | | 2.6568 | 28.0 | 2044 | 2.5862 | | 2.6568 | 29.0 | 2117 | 2.5828 | | 2.6568 | 30.0 | 2190 | 2.5765 | | 2.6568 | 31.0 | 2263 | 2.5742 | | 2.6568 | 32.0 | 2336 | 2.5682 | | 2.6568 | 33.0 | 2409 | 2.5656 | | 2.6568 | 34.0 | 2482 | 2.5614 | | 2.5489 | 35.0 | 2555 | 2.5605 | | 2.5489 | 36.0 | 2628 | 2.5552 | | 2.5489 | 37.0 | 2701 | 2.5541 | | 2.5489 | 38.0 | 2774 | 2.5494 | | 2.5489 | 39.0 | 2847 | 2.5491 | | 2.5489 | 40.0 | 2920 | 2.5455 | | 2.5489 | 41.0 | 2993 | 2.5452 | | 2.475 | 42.0 | 3066 | 2.5433 | | 2.475 | 43.0 | 3139 | 2.5397 | | 2.475 | 44.0 | 3212 | 2.5386 | | 2.475 | 45.0 | 3285 | 2.5400 | | 2.475 | 46.0 | 3358 | 2.5339 | | 2.475 | 47.0 | 3431 | 2.5327 | | 2.4144 | 48.0 | 3504 | 2.5327 | | 2.4144 | 49.0 | 3577 | 2.5312 | | 2.4144 | 50.0 | 3650 | 2.5338 | | 2.4144 | 51.0 | 3723 | 2.5314 | | 2.4144 | 52.0 | 3796 | 2.5309 | | 2.4144 | 53.0 | 3869 | 2.5289 | | 2.4144 | 54.0 | 3942 | 2.5290 | | 2.3642 | 55.0 | 4015 | 2.5270 | | 2.3642 | 56.0 | 4088 | 2.5270 | | 2.3642 | 57.0 | 4161 | 2.5263 | | 2.3642 | 58.0 | 4234 | 2.5267 | | 2.3642 | 59.0 | 4307 | 2.5273 | | 2.3642 | 60.0 | 4380 | 2.5258 | | 2.3642 | 61.0 | 4453 | 2.5253 | | 2.3216 | 62.0 | 4526 | 2.5244 | | 2.3216 | 63.0 | 4599 | 2.5256 | | 2.3216 | 64.0 | 4672 | 2.5227 | | 2.3216 | 65.0 | 4745 | 2.5241 | | 2.3216 | 66.0 | 4818 | 2.5244 | | 2.3216 | 67.0 | 4891 | 2.5236 | | 2.3216 | 68.0 | 4964 | 2.5251 | | 2.2879 | 69.0 | 5037 | 2.5231 | | 2.2879 | 70.0 | 5110 | 2.5254 | | 2.2879 | 71.0 | 5183 | 2.5242 | | 2.2879 | 72.0 | 5256 | 2.5254 | | 2.2879 | 73.0 | 5329 | 2.5253 | | 2.2879 | 74.0 | 5402 | 2.5228 | | 2.2879 | 75.0 | 5475 | 2.5247 | | 2.261 | 76.0 | 5548 | 2.5243 | | 2.261 | 77.0 | 5621 | 2.5247 | | 2.261 | 78.0 | 5694 | 2.5250 | | 2.261 | 79.0 | 5767 | 2.5248 | | 2.261 | 80.0 | 5840 | 2.5236 | | 2.261 | 81.0 | 5913 | 2.5264 | | 2.261 | 82.0 | 5986 | 2.5249 | | 2.2396 | 83.0 | 6059 | 2.5256 | | 2.2396 | 84.0 | 6132 | 2.5267 | | 2.2396 | 85.0 | 6205 | 2.5258 | | 2.2396 | 86.0 | 6278 | 2.5242 | | 2.2396 | 87.0 | 6351 | 2.5233 | | 2.2396 | 88.0 | 6424 | 2.5249 | | 2.2396 | 89.0 | 6497 | 2.5253 | | 2.2238 | 90.0 | 6570 | 2.5252 | | 2.2238 | 91.0 | 6643 | 2.5255 | | 2.2238 | 92.0 | 6716 | 2.5263 | | 2.2238 | 93.0 | 6789 | 2.5261 | | 2.2238 | 94.0 | 6862 | 2.5257 | | 2.2238 | 95.0 | 6935 | 2.5253 | | 2.213 | 96.0 | 7008 | 2.5267 | | 2.213 | 97.0 | 7081 | 2.5258 | | 2.213 | 98.0 | 7154 | 2.5258 | | 2.213 | 99.0 | 7227 | 2.5259 | | 2.213 | 100.0 | 7300 | 2.5260 | ### Framework versions - Transformers 4.8.1 - Pytorch 1.8.1+cu111 - Datasets 1.8.0 - Tokenizers 0.10.3

CennetOguz/distilbert-base-uncased-finetuned-recipe-accelerate

[ "pytorch", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

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7

null

## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/hindi-wav2vec2-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/hindi-wav2vec2-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```

Chaewon/mnmt_decoder_en_gpt2

[]

null

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0

null

--- license: apache-2.0 tags: - generated_from_trainer datasets: - itihasa model-index: - name: t5-base-finetuned-sn-to-en results: [] ---  # t5-base-finetuned-sn-to-en This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on the itihasa dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

Chakita/Friends

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

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8

"2021-12-19T16:52:53Z"

## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/addy88/wav2vec2-assamese-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/addy88/wav2vec2-assamese-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```

ChauhanVipul/BERT

[]

null

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0

null

## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/wav2vec2-punjabi-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/wav2vec2-punjabi-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```

Venkatakrishnan-Ramesh/Text_gen

[]

null

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0

null

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # aditeyabaral/sentencetransformer-roberta-base This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('aditeyabaral/sentencetransformer-roberta-base') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('aditeyabaral/sentencetransformer-roberta-base') model = AutoModel.from_pretrained('aditeyabaral/sentencetransformer-roberta-base') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=aditeyabaral/sentencetransformer-roberta-base) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 9234 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

ComCom/gpt2-large

[ "pytorch", "gpt2", "feature-extraction", "transformers" ]

feature-extraction

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1

null

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.10.2 - Pytorch 1.9.0+cu102 - Datasets 1.12.1 - Tokenizers 0.10.3

Contrastive-Tension/BERT-Distil-CT

[ "pytorch", "tf", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

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9

null

--- tags: - conversational --- # Rick DialoGPT medium model

Contrastive-Tension/BERT-Distil-NLI-CT

[ "pytorch", "tf", "distilbert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

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6

null

distilbert-base-uncased finetuned on the conll2003 dataset for NER.

Crasher222/kaggle-comp-test

[ "pytorch", "bert", "text-classification", "en", "dataset:Crasher222/autonlp-data-kaggle-test", "transformers", "autonlp", "co2_eq_emissions" ]

text-classification

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29

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] ---  # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

Crumped/imdb-simpleRNN

[ "keras" ]

null

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0

null

--- language: en tags: - financial-sentiment-analysis - sentiment-analysis datasets: - financial_phrasebank widget: - text: Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales. - text: Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000. - text: Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008. --- ### FinancialBERT for Sentiment Analysis [*FinancialBERT*](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that financial practitioners and researchers can benefit from this model without the necessity of the significant computational resources required to train the model. The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this model outperforms the general BERT and other financial domain-specific models. More details on `FinancialBERT`'s pre-training process can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining ### Training data FinancialBERT model was fine-tuned on [Financial PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). ### Fine-tuning hyper-parameters - learning_rate = 2e-5 - batch_size = 32 - max_seq_length = 512 - num_train_epochs = 5 ### Evaluation metrics The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the test set. | sentiment | precision | recall | f1-score | support | | ------------- |:-------------:|:-------------:|:-------------:| -----:| | negative | 0.96 | 0.97 | 0.97 | 58 | | neutral | 0.98 | 0.99 | 0.98 | 279 | | positive | 0.98 | 0.97 | 0.97 | 148 | | macro avg | 0.97 | 0.98 | 0.98 | 485 | | weighted avg | 0.98 | 0.98 | 0.98 | 485 | ### How to use The model can be used thanks to Transformers pipeline for sentiment analysis. ```python from transformers import BertTokenizer, BertForSequenceClassification from transformers import pipeline model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3) tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis") nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.", "Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000.", "Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008.", ] results = nlp(sentences) print(results) [{'label': 'positive', 'score': 0.9998133778572083}, {'label': 'neutral', 'score': 0.9997822642326355}, {'label': 'negative', 'score': 0.9877365231513977}] ``` > Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)

Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2-finetuned-de-to-is_nr2

[]

null

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0

null

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9260322366968425 - name: Recall type: recall value: 0.9383599955252265 - name: F1 type: f1 value: 0.9321553592265377 - name: Accuracy type: accuracy value: 0.9834146186474335 ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0607 - Precision: 0.9260 - Recall: 0.9384 - F1: 0.9322 - Accuracy: 0.9834 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2545 | 1.0 | 878 | 0.0711 | 0.9096 | 0.9214 | 0.9154 | 0.9800 | | 0.0555 | 2.0 | 1756 | 0.0593 | 0.9185 | 0.9356 | 0.9270 | 0.9827 | | 0.0297 | 3.0 | 2634 | 0.0607 | 0.9260 | 0.9384 | 0.9322 | 0.9834 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

DaWang/demo

[]

null

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0

null

--- license: apache-2.0 tags: - summarization - generated_from_trainer metrics: - rouge model-index: - name: mt5-small-wikinewssum-test results: [] ---  # mt5-small-wikinewssum-test This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9354 - Rouge1: 6.8433 - Rouge2: 2.5498 - Rougel: 5.6114 - Rougelsum: 6.353 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 12 - eval_batch_size: 12 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | No log | 1.0 | 661 | 3.2810 | 6.4161 | 2.403 | 5.3674 | 6.0329 | | No log | 2.0 | 1322 | 3.1515 | 6.9291 | 2.6826 | 5.6839 | 6.4359 | | No log | 3.0 | 1983 | 3.0565 | 6.7939 | 2.6113 | 5.6133 | 6.3126 | | No log | 4.0 | 2644 | 2.9815 | 6.0279 | 2.1637 | 4.9892 | 5.5962 | | No log | 5.0 | 3305 | 2.9645 | 6.3926 | 2.339 | 5.2716 | 5.9443 | | 3.9937 | 6.0 | 3966 | 2.9476 | 6.4739 | 2.3615 | 5.3473 | 6.0089 | | 3.9937 | 7.0 | 4627 | 2.9405 | 6.615 | 2.4309 | 5.4493 | 6.1445 | | 3.9937 | 8.0 | 5288 | 2.9354 | 6.8433 | 2.5498 | 5.6114 | 6.353 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.1 - Datasets 1.16.1 - Tokenizers 0.10.3

Darkrider/covidbert_mednli

[ "transformers" ]

null

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3

null

--- language: th datasets: - common_voice tags: - audio - automatic-speech-recognition - hf-asr-leaderboard - robust-speech-event - speech - xlsr-fine-tuning license: cc-by-sa-4.0 model-index: - name: XLS-R-53 - Thai results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: th metrics: - name: Test WER type: wer value: 0.9524 - name: Test SER type: ser value: 1.2346 - name: Test CER type: cer value: 0.1623 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: sv metrics: - name: Test WER type: wer value: null - name: Test SER type: ser value: null - name: Test CER type: cer value: null --- # `wav2vec2-large-xlsr-53-th` Finetuning `wav2vec2-large-xlsr-53` on Thai [Common Voice 7.0](https://commonvoice.mozilla.org/en/datasets) [Read more on our blog](https://medium.com/airesearch-in-th/airesearch-in-th-3c1019a99cd) We finetune [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) based on [Fine-tuning Wav2Vec2 for English ASR](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tuning_Wav2Vec2_for_English_ASR.ipynb) using Thai examples of [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets). The notebooks and scripts can be found in [vistec-ai/wav2vec2-large-xlsr-53-th](https://github.com/vistec-ai/wav2vec2-large-xlsr-53-th). The pretrained model and processor can be found at [airesearch/wav2vec2-large-xlsr-53-th](https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th). ## `robust-speech-event` Add `syllable_tokenize`, `word_tokenize` ([PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)) and [deepcut](https://github.com/rkcosmos/deepcut) tokenizers to `eval.py` from [robust-speech-event](https://github.com/huggingface/transformers/tree/master/examples/research_projects/robust-speech-event#evaluation) ``` > python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config th --split test --log_outputs --thai_tokenizer newmm/syllable/deepcut/cer ``` ### Eval results on Common Voice 7 "test": | | WER PyThaiNLP 2.3.1 | WER deepcut | SER | CER | |---------------------------------|---------------------|-------------|---------|---------| | Only Tokenization | 0.9524% | 2.5316% | 1.2346% | 0.1623% | | Cleaning rules and Tokenization | TBD | TBD | TBD | TBD | ## Usage ``` #load pretrained processor and model processor = Wav2Vec2Processor.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") model = Wav2Vec2ForCTC.from_pretrained("airesearch/wav2vec2-large-xlsr-53-th") #function to resample to 16_000 def speech_file_to_array_fn(batch, text_col="sentence", fname_col="path", resampling_to=16000): speech_array, sampling_rate = torchaudio.load(batch[fname_col]) resampler=torchaudio.transforms.Resample(sampling_rate, resampling_to) batch["speech"] = resampler(speech_array)[0].numpy() batch["sampling_rate"] = resampling_to batch["target_text"] = batch[text_col] return batch #get 2 examples as sample input 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) #infer with torch.no_grad(): logits = model(inputs.input_values,).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) >> Prediction: ['และ เขา ก็ สัมผัส ดีบุก', 'คุณ สามารถ รับทราบ เมื่อ ข้อความ นี้ ถูก อ่าน แล้ว'] >> Reference: ['และเขาก็สัมผัสดีบุก', 'คุณสามารถรับทราบเมื่อข้อความนี้ถูกอ่านแล้ว'] ``` ## Datasets Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) contains 133 validated hours of Thai (255 total hours) at 5GB. We pre-tokenize with `pythainlp.tokenize.word_tokenize`. We preprocess the dataset using cleaning rules described in `notebooks/cv-preprocess.ipynb` by [@tann9949](https://github.com/tann9949). We then deduplicate and split as described in [ekapolc/Thai_commonvoice_split](https://github.com/ekapolc/Thai_commonvoice_split) in order to 1) avoid data leakage due to random splits after cleaning in [Common Voice Corpus 7.0](https://commonvoice.mozilla.org/en/datasets) and 2) preserve the majority of the data for the training set. The dataset loading script is `scripts/th_common_voice_70.py`. You can use this scripts together with `train_cleand.tsv`, `validation_cleaned.tsv` and `test_cleaned.tsv` to have the same splits as we do. The resulting dataset is as follows: ``` DatasetDict({ train: Dataset({ features: ['path', 'sentence'], num_rows: 86586 }) test: Dataset({ features: ['path', 'sentence'], num_rows: 2502 }) validation: Dataset({ features: ['path', 'sentence'], num_rows: 3027 }) }) ``` ## Training We fintuned using the following configuration on a single V100 GPU and chose the checkpoint with the lowest validation loss. The finetuning script is `scripts/wav2vec2_finetune.py` ``` # create model model = Wav2Vec2ForCTC.from_pretrained( "facebook/wav2vec2-large-xlsr-53", attention_dropout=0.1, hidden_dropout=0.1, feat_proj_dropout=0.0, mask_time_prob=0.05, layerdrop=0.1, gradient_checkpointing=True, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, vocab_size=len(processor.tokenizer) ) model.freeze_feature_extractor() training_args = TrainingArguments( output_dir="../data/wav2vec2-large-xlsr-53-thai", group_by_length=True, per_device_train_batch_size=32, gradient_accumulation_steps=1, per_device_eval_batch_size=16, metric_for_best_model='wer', evaluation_strategy="steps", eval_steps=1000, logging_strategy="steps", logging_steps=1000, save_strategy="steps", save_steps=1000, num_train_epochs=100, fp16=True, learning_rate=1e-4, warmup_steps=1000, save_total_limit=3, report_to="tensorboard" ) ``` ## Evaluation We benchmark on the test set using WER with words tokenized by [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp) 2.3.1 and [deepcut](https://github.com/rkcosmos/deepcut), and CER. We also measure performance when spell correction using [TNC](http://www.arts.chula.ac.th/ling/tnc/) ngrams is applied. Evaluation codes can be found in `notebooks/wav2vec2_finetuning_tutorial.ipynb`. Benchmark is performed on `test-unique` split. | | WER PyThaiNLP 2.3.1 | WER deepcut | CER | |--------------------------------|---------------------|----------------|----------------| | [Kaldi from scratch](https://github.com/vistec-AI/commonvoice-th) | 23.04 | | 7.57 | | Ours without spell correction | 13.634024 | **8.152052** | **2.813019** | | Ours with spell correction | 17.996397 | 14.167975 | 5.225761 | | Google Web Speech API※ | 13.711234 | 10.860058 | 7.357340 | | Microsoft Bing Speech API※ | **12.578819** | 9.620991 | 5.016620 | | Amazon Transcribe※ | 21.86334 | 14.487553 | 7.077562 | | NECTEC AI for Thai Partii API※ | 20.105887 | 15.515631 | 9.551027 | ※ APIs are not finetuned with Common Voice 7.0 data ## LICENSE [cc-by-sa 4.0](https://github.com/vistec-AI/wav2vec2-large-xlsr-53-th/blob/main/LICENSE) ## Ackowledgements * model training and validation notebooks/scripts [@cstorm125](https://github.com/cstorm125/) * dataset cleaning scripts [@tann9949](https://github.com/tann9949) * dataset splits [@ekapolc](https://github.com/ekapolc/) and [@14mss](https://github.com/14mss) * running the training [@mrpeerat](https://github.com/mrpeerat) * spell correction [@wannaphong](https://github.com/wannaphong)

Darren/darren

[ "pytorch" ]

null

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0

null

--- widget: - text: "สวนกุหลาบเป็นโรงเรียนอะไร" context: "โรงเรียนสวนกุหลาบวิทยาลัย (Suankularb Wittayalai School) (อักษรย่อ : ส.ก. / S.K.) เป็นโรงเรียนชายล้วน ระดับชั้นมัธยมศึกษาขนาดใหญ่พิเศษ สังกัดสำนักงานเขตพื้นที่การศึกษามัธยมศึกษาเขต 1 สำนักงานคณะกรรมการการศึกษาขั้นพื้นฐาน (ชื่อเดิม: กรมสามัญศึกษา) กระทรวงศึกษาธิการ ก่อตั้งโดย พระบาทสมเด็จพระจุลจอมเกล้าเจ้าอยู่หัว ได้รับการสถาปนาขึ้นในวันที่ 8 มีนาคม พ.ศ. 2424 (ขณะนั้นนับวันที่ 1 เมษายน เป็นวันขึ้นปีใหม่ เมื่อนับอย่างสากลถือเป็น พ.ศ. 2425) โดยเป็นโรงเรียนรัฐบาลแห่งแรกของประเทศไทย" --- # xlm-roberta-base-finetune-qa Finetuning `xlm-roberta-base` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`. Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py). Train with: ``` export WANDB_PROJECT=wangchanberta-qa export MODEL_NAME=xlm-roberta-base python train_question_answering_lm_finetuning.py \ --model_name $MODEL_NAME \ --dataset_name chimera_qa \ --output_dir $MODEL_NAME-finetune-chimera_qa-model \ --log_dir $MODEL_NAME-finetune-chimera_qa-log \ --pad_on_right \ --fp16 ```

DarshanDeshpande/marathi-distilbert

[ "pytorch", "tf", "distilbert", "fill-mask", "mr", "dataset:Oscar Corpus, News, Stories", "arxiv:1910.01108", "transformers", "license:apache-2.0", "autotrain_compatible" ]

fill-mask

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14

null

# Finetuend `xlm-roberta-base` model on Thai sequence and token classification datasets Finetuned XLM Roberta BASE model on Thai sequence and token classification datasets The script and documentation can be found at [this repository](https://github.com/vistec-AI/thai2transformers). ## Model description We use the pretrained cross-lingual RoBERTa model as proposed by [[Conneau et al., 2020]](https://arxiv.org/abs/1911.02116). We download the pretrained PyTorch model via HuggingFace's Model Hub (https://huggingface.co/xlm-roberta-base) ## Intended uses & limitations You can use the finetuned models for multiclass/multilabel text classification and token classification task. **Multiclass text classification** - `wisesight_sentiment` 4-class text classification task (`positive`, `neutral`, `negative`, and `question`) based on social media posts and tweets. - `wongnai_reivews` Users' review rating classification task (scale is ranging from 1 to 5) - `generated_reviews_enth` : (`review_star` as label) Generated users' review rating classification task (scale is ranging from 1 to 5). **Multilabel text classification** - `prachathai67k` Thai topic classification with 12 labels based on news article corpus from prachathai.com. The detail is described in this [page](https://huggingface.co/datasets/prachathai67k). **Token classification** - `thainer` Named-entity recognition tagging with 13 named-entities as descibed in this [page](https://huggingface.co/datasets/thainer). - `lst20` : NER NER and POS tagging Named-entity recognition tagging with 10 named-entities and Part-of-Speech tagging with 16 tags as descibed in this [page](https://huggingface.co/datasets/lst20). ## How to use The example notebook demonstrating how to use finetuned model for inference can be found at this [Colab notebook](https://colab.research.google.com/drive/1Kbk6sBspZLwcnOE61adAQo30xxqOQ9ko) **BibTeX entry and citation info** ``` @misc{lowphansirikul2021wangchanberta, title={WangchanBERTa: Pretraining transformer-based Thai Language Models}, author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong}, year={2021}, eprint={2101.09635}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

DataikuNLP/average_word_embeddings_glove.6B.300d

[ "arxiv:1908.10084", "sentence-transformers", "feature-extraction", "sentence-similarity", "license:apache-2.0" ]

sentence-similarity

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0

null

--- tags: - conversational --- # Michael Scott DialoGPT Model

DataikuNLP/camembert-base

[ "pytorch", "tf", "camembert", "fill-mask", "fr", "dataset:oscar", "arxiv:1911.03894", "transformers", "license:mit", "autotrain_compatible" ]

fill-mask

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8

null

--- tags: - conversational --- # Harry Potter DialoGPT Model

Davlan/byt5-base-eng-yor-mt

[ "pytorch", "t5", "text2text-generation", "arxiv:2103.08647", "transformers", "autotrain_compatible" ]

text2text-generation

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11

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53-AL-colab results: [] ---  # wav2vec2-large-xlsr-53-AL-colab This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5358 - Wer: 0.5443 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9391 | 0.4 | 400 | 2.0722 | 0.9249 | | 0.8775 | 0.8 | 800 | 1.7171 | 0.6778 | | 0.665 | 1.2 | 1200 | 1.7250 | 0.6235 | | 0.6135 | 1.6 | 1600 | 1.4021 | 0.5847 | | 0.5795 | 2.0 | 2000 | 1.6191 | 0.5696 | | 0.5031 | 2.4 | 2400 | 1.6767 | 0.5586 | | 0.4933 | 2.8 | 2800 | 1.5358 | 0.5443 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3

Davlan/byt5-base-yor-eng-mt

[ "pytorch", "t5", "text2text-generation", "arxiv:2103.08647", "transformers", "autotrain_compatible" ]

text2text-generation

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12

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53-AL results: [] ---  # wav2vec2-large-xlsr-53-AL This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2712 - Wer: 0.6940 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.073 | 8.0 | 200 | 1.0990 | 0.7002 | | 0.0561 | 16.0 | 400 | 1.1455 | 0.6805 | | 0.0378 | 24.0 | 600 | 1.2712 | 0.6940 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

Davlan/distilbert-base-multilingual-cased-masakhaner

[ "pytorch", "tf", "distilbert", "token-classification", "arxiv:2103.11811", "transformers", "autotrain_compatible" ]

token-classification

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16

null

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53-Total results: [] ---  # wav2vec2-large-xlsr-53-Total This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2814 - Wer: 0.2260 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.9157 | 0.2 | 400 | 2.8204 | 0.9707 | | 0.9554 | 0.4 | 800 | 0.5295 | 0.5046 | | 0.7585 | 0.6 | 1200 | 0.4007 | 0.3850 | | 0.7288 | 0.8 | 1600 | 0.3632 | 0.3447 | | 0.6792 | 1.0 | 2000 | 0.3433 | 0.3216 | | 0.6085 | 1.2 | 2400 | 0.3254 | 0.2928 | | 0.6225 | 1.4 | 2800 | 0.3161 | 0.2832 | | 0.6183 | 1.6 | 3200 | 0.3111 | 0.2721 | | 0.5947 | 1.8 | 3600 | 0.2969 | 0.2615 | | 0.5953 | 2.0 | 4000 | 0.2912 | 0.2515 | | 0.5358 | 2.2 | 4400 | 0.2920 | 0.2501 | | 0.5535 | 2.4 | 4800 | 0.2939 | 0.2538 | | 0.5408 | 2.6 | 5200 | 0.2854 | 0.2452 | | 0.5272 | 2.8 | 5600 | 0.2816 | 0.2434 | | 0.5248 | 3.0 | 6000 | 0.2755 | 0.2354 | | 0.4923 | 3.2 | 6400 | 0.2795 | 0.2353 | | 0.489 | 3.4 | 6800 | 0.2767 | 0.2330 | | 0.4932 | 3.6 | 7200 | 0.2821 | 0.2335 | | 0.4841 | 3.8 | 7600 | 0.2756 | 0.2349 | | 0.4794 | 4.0 | 8000 | 0.2751 | 0.2265 | | 0.444 | 4.2 | 8400 | 0.2809 | 0.2283 | | 0.4533 | 4.4 | 8800 | 0.2804 | 0.2312 | | 0.4563 | 4.6 | 9200 | 0.2830 | 0.2256 | | 0.4498 | 4.8 | 9600 | 0.2819 | 0.2251 | | 0.4532 | 5.0 | 10000 | 0.2814 | 0.2260 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3

Davlan/mt5_base_eng_yor_mt

[ "pytorch", "mt5", "text2text-generation", "arxiv:2103.08647", "transformers", "autotrain_compatible" ]

text2text-generation

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2

null

--- language: "id" widget: - text: "dia orang yang baik ya bunds." --- ## how to use ```python from transformers import pipeline, set_seed path = "akahana/indonesia-emotion-roberta" emotion = pipeline('text-classification', model=path,device=0) set_seed(42) kalimat = "dia orang yang baik ya bunds." preds = emotion(kalimat) preds [{'label': 'BAHAGIA', 'score': 0.8790940046310425}] ```

Davlan/xlm-roberta-base-finetuned-naija

[ "pytorch", "xlm-roberta", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "XLMRobertaForMaskedLM" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

1

null

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-tamil-colab results: [] ---  # wav2vec2-large-xls-r-300m-tamil-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.8072 - Wer: 0.6531 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 11.0967 | 1.0 | 118 | 4.6437 | 1.0 | | 3.4973 | 2.0 | 236 | 3.2588 | 1.0 | | 3.1305 | 3.0 | 354 | 2.6566 | 1.0 | | 1.2931 | 4.0 | 472 | 0.9156 | 0.9944 | | 0.6851 | 5.0 | 590 | 0.7474 | 0.8598 | | 0.525 | 6.0 | 708 | 0.6649 | 0.7995 | | 0.4325 | 7.0 | 826 | 0.6740 | 0.7752 | | 0.3766 | 8.0 | 944 | 0.6220 | 0.7628 | | 0.3256 | 9.0 | 1062 | 0.6316 | 0.7322 | | 0.2802 | 10.0 | 1180 | 0.6442 | 0.7305 | | 0.2575 | 11.0 | 1298 | 0.6885 | 0.7280 | | 0.2248 | 12.0 | 1416 | 0.6702 | 0.7197 | | 0.2089 | 13.0 | 1534 | 0.6781 | 0.7173 | | 0.1893 | 14.0 | 1652 | 0.6981 | 0.7049 | | 0.1652 | 15.0 | 1770 | 0.7154 | 0.7436 | | 0.1643 | 16.0 | 1888 | 0.6798 | 0.7023 | | 0.1472 | 17.0 | 2006 | 0.7381 | 0.6947 | | 0.1372 | 18.0 | 2124 | 0.7240 | 0.7065 | | 0.1318 | 19.0 | 2242 | 0.7305 | 0.6714 | | 0.1211 | 20.0 | 2360 | 0.7288 | 0.6597 | | 0.1178 | 21.0 | 2478 | 0.7417 | 0.6699 | | 0.1118 | 22.0 | 2596 | 0.7476 | 0.6753 | | 0.1016 | 23.0 | 2714 | 0.7973 | 0.6647 | | 0.0998 | 24.0 | 2832 | 0.8027 | 0.6633 | | 0.0917 | 25.0 | 2950 | 0.8045 | 0.6680 | | 0.0907 | 26.0 | 3068 | 0.7884 | 0.6565 | | 0.0835 | 27.0 | 3186 | 0.8009 | 0.6622 | | 0.0749 | 28.0 | 3304 | 0.8123 | 0.6536 | | 0.0755 | 29.0 | 3422 | 0.8006 | 0.6555 | | 0.074 | 30.0 | 3540 | 0.8072 | 0.6531 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

Davlan/xlm-roberta-large-masakhaner

[ "pytorch", "tf", "xlm-roberta", "token-classification", "arxiv:2103.11811", "transformers", "autotrain_compatible" ]

token-classification

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1,449

null

--- language: en datasets: - cuad --- # Model Card for RoBERTa Large Model fine-tuned with CUAD dataset This model is the fine-tuned version of "RoBERTa Large" using CUAD dataset # Model Details ## Model Description The [Contract Understanding Atticus Dataset (CUAD)](https://www.atticusprojectai.org/cuad), pronounced "kwad", a dataset for legal contract review curated by the Atticus Project. Contract review is a task about "finding needles in a haystack." We find that Transformer models have nascent performance on CUAD, but that this performance is strongly influenced by model design and training dataset size. Despite some promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community. - **Developed by:** TheAtticusProject - **Shared by [Optional]:** HuggingFace - **Model type:** Language model - **Language(s) (NLP):** en - **License:** More information needed - **Related Models:** RoBERTA - **Parent Model:**RoBERTA Large - **Resources for more information:** - [GitHub Repo](https://github.com/TheAtticusProject/cuad) - [Associated Paper](https://arxiv.org/abs/2103.06268) # Uses ## Direct Use Legal contract review ## Downstream Use [Optional] More information needed ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations. # Training Details ## Training Data See [cuad dataset card](https://huggingface.co/datasets/cuad) for further details ## Training Procedure More information needed ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data #### Extra Data Researchers may be interested in several gigabytes of unlabeled contract pretraining data, which is available [here](https://drive.google.com/file/d/1of37X0hAhECQ3BN_004D8gm6V88tgZaB/view?usp=sharing). ### Factors More information needed ### Metrics More information needed ## Results We [provide checkpoints](https://zenodo.org/record/4599830) for three of the best models fine-tuned on CUAD: RoBERTa-base (~100M parameters), RoBERTa-large (~300M parameters), and DeBERTa-xlarge (~900M parameters). # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software The HuggingFace [Transformers](https://huggingface.co/transformers) library. It was tested with Python 3.8, PyTorch 1.7, and Transformers 4.3/4.4. # Citation **BibTeX:** @article{hendrycks2021cuad, title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review}, author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball}, journal={NeurIPS}, year={2021} } # Glossary [optional] More information needed # More Information [optional] For more details about CUAD and legal contract review, see the [Atticus Project website](https://www.atticusprojectai.org/cuad). # Model Card Authors [optional] TheAtticusProject # Model Card Contact [TheAtticusProject](https://www.atticusprojectai.org/), in collaboration with the Ezi Ozoani and the HuggingFace Team # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("akdeniz27/roberta-large-cuad") model = AutoModelForQuestionAnswering.from_pretrained("akdeniz27/roberta-large-cuad") ``` </details>

Davlan/xlm-roberta-large-ner-hrl

[ "pytorch", "tf", "xlm-roberta", "token-classification", "transformers", "autotrain_compatible" ]

token-classification

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1,322

null

--- language: tr widget: - text: "Mustafa Kemal Atatürk 19 Mayıs 1919'da Samsun'a çıktı." --- # Turkish Named Entity Recognition (NER) Model This model is the fine-tuned version of "xlm-roberta-base" (a multilingual version of RoBERTa) using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). # Fine-tuning parameters: ``` task = "ner" model_checkpoint = "xlm-roberta-base" batch_size = 8 label_list = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] max_length = 512 learning_rate = 2e-5 num_train_epochs = 2 weight_decay = 0.01 ``` # How to use: ``` model = AutoModelForTokenClassification.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner") tokenizer = AutoTokenizer.from_pretrained("akdeniz27/xlm-roberta-base-turkish-ner") ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") ner("<your text here>") ``` Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. # Reference test results: * accuracy: 0.9919343118732742 * f1: 0.9492100796448622 * precision: 0.9407349896480332 * recall: 0.9578392621870883

Declan/CNN_model_v3

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- language: "ar" tags: - text-generation datasets: - Arabic poetry from several eras --- # GPT2-Small-Arabic-Poetry ## Model description Fine-tuned model of Arabic poetry dataset based on gpt2-small-arabic. ## Intended uses & limitations #### How to use An example is provided in this [colab notebook](https://colab.research.google.com/drive/1mRl7c-5v-Klx27EEAEOAbrfkustL4g7a?usp=sharing). #### Limitations and bias Both the GPT2-small-arabic (trained on Arabic Wikipedia) and this model have several limitations in terms of coverage and training performance. Use them as demonstrations or proof of concepts but not as production code. ## Training data This pretrained model used the [Arabic Poetry dataset](https://www.kaggle.com/ahmedabelal/arabic-poetry) from 9 different eras with a total of around 40k poems. The dataset was trained (fine-tuned) based on the [gpt2-small-arabic](https://huggingface.co/akhooli/gpt2-small-arabic) transformer model. ## Training procedure Training was done using [Simple Transformers](https://github.com/ThilinaRajapakse/simpletransformers) library on Kaggle, using free GPU. ## Eval results Final perplexity reached ws 76.3, loss: 4.33 ### BibTeX entry and citation info ```bibtex @inproceedings{Abed Khooli, year={2020} } ```

Declan/CNN_model_v5

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

"2020-09-06T15:15:36Z"

--- tags: - translation language: - ar - en license: mit --- ### mbart-large-ar-en This is mbart-large-cc25, finetuned on a subset of the OPUS corpus for ar_en. Usage: see [example notebook](https://colab.research.google.com/drive/1I6RFOWMaTpPBX7saJYjnSTddW0TD6H1t?usp=sharing) Note: model has limited training set, not fully trained (do not use for production). Other models by me: [Abed Khooli](https://huggingface.co/akhooli)

Declan/FoxNews_model_v1

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: mit tags: - generated_from_trainer model-index: - name: conserv_fulltext_model results: [] ---  # conserv_fulltext_model This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3 unbalanced_texts gpt2

Declan/FoxNews_model_v8

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-gpt2 Changes: use old format for `pytorch_model.bin`.

Declan/HuffPost_model_v1

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mbart Changes: use old format for `pytorch_model.bin`.

Declan/HuffPost_model_v2

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-mpnet Changes: use old format for `pytorch_model.bin`.

Declan/HuffPost_model_v4

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

This is a copy of: https://huggingface.co/hf-internal-testing/tiny-random-xlnet Changes: use old format for `pytorch_model.bin`.

Declan/HuffPost_model_v8

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - matthews_correlation model_index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification metric: name: Matthews Correlation type: matthews_correlation value: 0.6290322580645161 ---  # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 1.0475 - Matthews Correlation: 0.6290 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | No log | 1.0 | 16 | 1.3863 | 0.0 | | No log | 2.0 | 32 | 1.2695 | 0.4503 | | No log | 3.0 | 48 | 1.1563 | 0.6110 | | No log | 4.0 | 64 | 1.0757 | 0.6290 | | No log | 5.0 | 80 | 1.0475 | 0.6290 | ### Framework versions - Transformers 4.9.2 - Pytorch 1.9.0+cu102 - Datasets 1.11.0 - Tokenizers 0.10.3

Declan/NPR_model_v2

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-cloud-ner results: [] ---  # distilbert-base-uncased-finetuned-cloud-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0812 - Precision: 0.8975 - Recall: 0.9080 - F1: 0.9027 - Accuracy: 0.9703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 166 | 0.1326 | 0.7990 | 0.8043 | 0.8017 | 0.9338 | | No log | 2.0 | 332 | 0.0925 | 0.8770 | 0.8946 | 0.8858 | 0.9618 | | No log | 3.0 | 498 | 0.0812 | 0.8975 | 0.9080 | 0.9027 | 0.9703 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

Declan/NPR_model_v3

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-cloud1-ner results: [] ---  # distilbert-base-uncased-finetuned-cloud1-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0074 - Precision: 0.9714 - Recall: 0.9855 - F1: 0.9784 - Accuracy: 0.9972 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 166 | 0.0160 | 0.9653 | 0.9420 | 0.9535 | 0.9945 | | No log | 2.0 | 332 | 0.0089 | 0.9623 | 0.9855 | 0.9737 | 0.9965 | | No log | 3.0 | 498 | 0.0074 | 0.9714 | 0.9855 | 0.9784 | 0.9972 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

Declan/NPR_model_v4

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-cloud2-ner results: [] ---  # distilbert-base-uncased-finetuned-cloud2-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8866 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.8453 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 162 | 0.7804 | 0.0 | 0.0 | 0.0 | 0.8447 | | No log | 2.0 | 324 | 0.8303 | 0.0 | 0.0 | 0.0 | 0.8465 | | No log | 3.0 | 486 | 0.8866 | 0.0 | 0.0 | 0.0 | 0.8453 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

Declan/NPR_model_v6

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9988 - Precision: 0.3 - Recall: 0.6 - F1: 0.4 - Accuracy: 0.7870 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 84 | 0.8399 | 0.2105 | 0.4 | 0.2759 | 0.75 | | No log | 2.0 | 168 | 0.9664 | 0.3 | 0.6 | 0.4 | 0.7870 | | No log | 3.0 | 252 | 0.9988 | 0.3 | 0.6 | 0.4 | 0.7870 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

Declan/WallStreetJournal_model_v2

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

7

null

--- tags: autonlp language: en widget: - text: "I love AutoNLP 🤗" datasets: - alecmullen/autonlp-data-group-classification co2_eq_emissions: 0.4362732160754736 --- # Model Trained Using AutoNLP - Problem type: Multi-class Classification - Model ID: 441411446 - CO2 Emissions (in grams): 0.4362732160754736 ## Validation Metrics - Loss: 0.7598486542701721 - Accuracy: 0.8222222222222222 - Macro F1: 0.2912091747693842 - Micro F1: 0.8222222222222222 - Weighted F1: 0.7707160863181806 - Macro Precision: 0.29631463146314635 - Micro Precision: 0.8222222222222222 - Weighted Precision: 0.7341339689524508 - Macro Recall: 0.30174603174603176 - Micro Recall: 0.8222222222222222 - Weighted Recall: 0.8222222222222222 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/alecmullen/autonlp-group-classification-441411446 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("alecmullen/autonlp-group-classification-441411446", use_auth_token=True) inputs = tokenizer("I love AutoNLP", return_tensors="pt") outputs = model(**inputs) ```

DeltaHub/adapter_t5-3b_mrpc

[ "pytorch", "transformers" ]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 model-index: - name: t5-small-finetuned300-en-to-de results: [] ---  # t5-small-finetuned300-en-to-de This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | No log | 1.0 | 136 | 1.1454 | 14.2319 | 17.8329 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

DeltaHub/lora_t5-base_mrpc

[ "pytorch", "transformers" ]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 model-index: - name: t5-small-finetuned8-en-to-de results: [] ---  # t5-small-finetuned8-en-to-de This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| | No log | 1.0 | 136 | 3.6717 | 3.9127 | 4.0207 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

Deniskin/essays_small_2000

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- language: - ru multilinguality: - monolingual widget: - text: "Жалобы на боль внизу <mask> после приёма пищи." example_title: "pain_example" - text: "Пациентка наблюдалась у <mask> по поводу грибкового поражения кожи." example_title: "spec_example" - text: "Появился зуд тела, <mask> веса, потливость, проводил контроль сахара крови." example_title: "weight_example" --- Paper: https://arxiv.org/abs/2204.03951 Code: https://github.com/alexyalunin/RuBioRoBERTa ### Citation ``` @misc{alex2022rubioroberta, title={RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining}, author={Alexander Yalunin and Alexander Nesterov and Dmitriy Umerenkov}, year={2022}, eprint={2204.03951}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

Deniskin/gpt3_medium

[ "pytorch", "gpt2", "text-generation", "transformers", "has_space" ]

text-generation

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

52

null

--- language: - ar - dz tags: - pytorch - bert - multilingual - ar - dz license: apache-2.0 widget: - text: " أنا من الجزائر من ولاية [MASK] " - text: "rabi [MASK] khouya sami" - text: " ربي [MASK] خويا لعزيز" - text: "tahya el [MASK]." - text: "rouhi ya dzayer [MASK]" inference: true --- <img src="https://raw.githubusercontent.com/alger-ia/dziribert/main/dziribert_drawing.png" alt="drawing" width="25%" height="25%" align="right"/> # DziriBERT DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian text contents written using both Arabic and Latin characters. It sets new state of the art results on Algerian text classification datasets, even if it has been pre-trained on much less data (~1 million tweets). For more information, please visit our paper: https://arxiv.org/pdf/2109.12346.pdf. ## How to use ```python from transformers import BertTokenizer, BertForMaskedLM tokenizer = BertTokenizer.from_pretrained("alger-ia/dziribert") model = BertForMaskedLM.from_pretrained("alger-ia/dziribert") ``` You can find a fine-tuning script in our Github repo: https://github.com/alger-ia/dziribert ## Limitations The pre-training data used in this project comes from social media (Twitter). Therefore, the Masked Language Modeling objective may predict offensive words in some situations. Modeling this kind of words may be either an advantage (e.g. when training a hate speech model) or a disadvantage (e.g. when generating answers that are directly sent to the end user). Depending on your downstream task, you may need to filter out such words especially when returning automatically generated text to the end user. ### How to cite ```bibtex @article{dziribert, title={DziriBERT: a Pre-trained Language Model for the Algerian Dialect}, author={Abdaoui, Amine and Berrimi, Mohamed and Oussalah, Mourad and Moussaoui, Abdelouahab}, journal={arXiv preprint arXiv:2109.12346}, year={2021} } ``` ## Contact Please contact [email protected] for any question, feedback or request.

DeskDown/MarianMixFT_en-vi

[ "pytorch", "marian", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MarianMTModel" ], "model_type": "marian", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-uncased_token_itr0_0.0001_all_01_03_2022-04_48_27 results: [] ---  # bert-base-uncased_token_itr0_0.0001_all_01_03_2022-04_48_27 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2899 - Precision: 0.3170 - Recall: 0.5261 - F1: 0.3956 - Accuracy: 0.8799 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.2912 | 0.2752 | 0.4444 | 0.3400 | 0.8730 | | No log | 2.0 | 60 | 0.2772 | 0.4005 | 0.4589 | 0.4277 | 0.8911 | | No log | 3.0 | 90 | 0.2267 | 0.3642 | 0.5281 | 0.4311 | 0.9043 | | No log | 4.0 | 120 | 0.2129 | 0.3617 | 0.5455 | 0.4350 | 0.9140 | | No log | 5.0 | 150 | 0.2399 | 0.3797 | 0.5556 | 0.4511 | 0.9114 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Devid/DialoGPT-small-Miku

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

10

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_BERT_token_itr0_0.0001_editorials_01_03_2022-15_50_21 results: [] ---  # correct_BERT_token_itr0_0.0001_editorials_01_03_2022-15_50_21 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1059 - Precision: 0.0637 - Recall: 0.0080 - F1: 0.0141 - Accuracy: 0.9707 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.1103 | 0.12 | 0.0135 | 0.0243 | 0.9772 | | No log | 2.0 | 30 | 0.0842 | 0.12 | 0.0135 | 0.0243 | 0.9772 | | No log | 3.0 | 45 | 0.0767 | 0.12 | 0.0135 | 0.0243 | 0.9772 | | No log | 4.0 | 60 | 0.0754 | 0.12 | 0.0135 | 0.0243 | 0.9772 | | No log | 5.0 | 75 | 0.0735 | 0.12 | 0.0135 | 0.0243 | 0.9772 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Devmapall/paraphrase-quora

[ "pytorch", "jax", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "T5ForConditionalGeneration" ], "model_type": "t5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": true, "length_penalty": 2, "max_length": 200, "min_length": 30, "no_repeat_ngram_size": 3, "num_beams": 4, "prefix": "summarize: " }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to German: " }, "translation_en_to_fr": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to French: " }, "translation_en_to_ro": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to Romanian: " } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47 results: [] ---  # correct_BERT_token_itr0_0.0001_essays_01_03_2022-15_48_47 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1801 - Precision: 0.6153 - Recall: 0.7301 - F1: 0.6678 - Accuracy: 0.9346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 11 | 0.2746 | 0.4586 | 0.5922 | 0.5169 | 0.9031 | | No log | 2.0 | 22 | 0.2223 | 0.5233 | 0.6181 | 0.5668 | 0.9148 | | No log | 3.0 | 33 | 0.2162 | 0.5335 | 0.6699 | 0.5940 | 0.9274 | | No log | 4.0 | 44 | 0.2053 | 0.5989 | 0.7055 | 0.6478 | 0.9237 | | No log | 5.0 | 55 | 0.2123 | 0.5671 | 0.7249 | 0.6364 | 0.9267 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Devrim/prism-default

[ "license:mit" ]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_BERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_47_14 results: [] ---  # correct_BERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_47_14 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6542 - Precision: 0.0092 - Recall: 0.0403 - F1: 0.0150 - Accuracy: 0.7291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.5856 | 0.0012 | 0.0125 | 0.0022 | 0.6950 | | No log | 2.0 | 20 | 0.5933 | 0.0 | 0.0 | 0.0 | 0.7282 | | No log | 3.0 | 30 | 0.5729 | 0.0051 | 0.025 | 0.0085 | 0.7155 | | No log | 4.0 | 40 | 0.6178 | 0.0029 | 0.0125 | 0.0047 | 0.7143 | | No log | 5.0 | 50 | 0.6707 | 0.0110 | 0.0375 | 0.0170 | 0.7178 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

DevsIA/Devs_IA

[]

null

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0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_distilBERT_token_itr0_1e-05_all_01_03_2022-15_43_47 results: [] ---  # correct_distilBERT_token_itr0_1e-05_all_01_03_2022-15_43_47 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3343 - Precision: 0.1651 - Recall: 0.3039 - F1: 0.2140 - Accuracy: 0.8493 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4801 | 0.0352 | 0.0591 | 0.0441 | 0.7521 | | No log | 2.0 | 60 | 0.3795 | 0.0355 | 0.0795 | 0.0491 | 0.8020 | | No log | 3.0 | 90 | 0.3359 | 0.0591 | 0.1294 | 0.0812 | 0.8334 | | No log | 4.0 | 120 | 0.3205 | 0.0785 | 0.1534 | 0.1039 | 0.8486 | | No log | 5.0 | 150 | 0.3144 | 0.0853 | 0.1571 | 0.1105 | 0.8516 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

DevsIA/imagenes

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 results: [] ---  # correct_distilBERT_token_itr0_1e-05_editorials_01_03_2022-15_42_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1206 - Precision: 0.0637 - Recall: 0.0080 - F1: 0.0141 - Accuracy: 0.9707 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 15 | 0.1222 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 2.0 | 30 | 0.1159 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 3.0 | 45 | 0.1082 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 4.0 | 60 | 0.1042 | 0.12 | 0.0139 | 0.0249 | 0.9736 | | No log | 5.0 | 75 | 0.1029 | 0.12 | 0.0139 | 0.0249 | 0.9736 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

DewiBrynJones/wav2vec2-large-xlsr-welsh

[ "cy", "dataset:common_voice", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "license:apache-2.0", "model-index" ]

automatic-speech-recognition

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_distilBERT_token_itr0_1e-05_essays_01_03_2022-15_41_29 results: [] ---  # correct_distilBERT_token_itr0_1e-05_essays_01_03_2022-15_41_29 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3097 - Precision: 0.2769 - Recall: 0.4391 - F1: 0.3396 - Accuracy: 0.8878 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 11 | 0.4573 | 0.0094 | 0.0027 | 0.0042 | 0.7702 | | No log | 2.0 | 22 | 0.3660 | 0.1706 | 0.3253 | 0.2239 | 0.8516 | | No log | 3.0 | 33 | 0.3096 | 0.2339 | 0.408 | 0.2974 | 0.8827 | | No log | 4.0 | 44 | 0.2868 | 0.2963 | 0.4693 | 0.3633 | 0.8928 | | No log | 5.0 | 55 | 0.2798 | 0.3141 | 0.48 | 0.3797 | 0.8960 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

DheerajPranav/Dialo-GPT-Rick-bot

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_40_24 results: [] ---  # correct_distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_40_24 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5794 - Precision: 0.0094 - Recall: 0.0147 - F1: 0.0115 - Accuracy: 0.7156 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.6319 | 0.08 | 0.0312 | 0.0449 | 0.6753 | | No log | 2.0 | 20 | 0.6265 | 0.0364 | 0.0312 | 0.0336 | 0.6764 | | No log | 3.0 | 30 | 0.6216 | 0.0351 | 0.0312 | 0.0331 | 0.6762 | | No log | 4.0 | 40 | 0.6193 | 0.0274 | 0.0312 | 0.0292 | 0.6759 | | No log | 5.0 | 50 | 0.6183 | 0.0222 | 0.0312 | 0.0260 | 0.6773 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Dhito/am

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

"2022-03-01T14:36:14Z"

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04 results: [] ---  # correct_twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_36_04 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2876 - Precision: 0.2345 - Recall: 0.4281 - F1: 0.3030 - Accuracy: 0.8728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.3907 | 0.0433 | 0.0824 | 0.0568 | 0.7626 | | No log | 2.0 | 60 | 0.3046 | 0.2302 | 0.4095 | 0.2947 | 0.8598 | | No log | 3.0 | 90 | 0.2945 | 0.2084 | 0.4095 | 0.2762 | 0.8668 | | No log | 4.0 | 120 | 0.2687 | 0.2847 | 0.4607 | 0.3519 | 0.8761 | | No log | 5.0 | 150 | 0.2643 | 0.2779 | 0.4444 | 0.3420 | 0.8788 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Dhruva/Interstellar

[]

null

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0

null

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-15_32_16 results: [] ---  # correct_twitter_RoBERTa_token_itr0_1e-05_essays_01_03_2022-15_32_16 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2663 - Precision: 0.3644 - Recall: 0.4985 - F1: 0.4210 - Accuracy: 0.8997 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 11 | 0.5174 | 0.0120 | 0.0061 | 0.0081 | 0.6997 | | No log | 2.0 | 22 | 0.4029 | 0.1145 | 0.3098 | 0.1672 | 0.8265 | | No log | 3.0 | 33 | 0.3604 | 0.2539 | 0.4448 | 0.3233 | 0.8632 | | No log | 4.0 | 44 | 0.3449 | 0.2992 | 0.4755 | 0.3673 | 0.8704 | | No log | 5.0 | 55 | 0.3403 | 0.3340 | 0.4816 | 0.3945 | 0.8760 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Dibyaranjan/nl_image_search

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: correct_twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-15_30_39 results: [] ---  # correct_twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-15_30_39 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6169 - Precision: 0.0031 - Recall: 0.0357 - F1: 0.0057 - Accuracy: 0.6464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.6339 | 0.0116 | 0.0120 | 0.0118 | 0.6662 | | No log | 2.0 | 20 | 0.6182 | 0.0064 | 0.0120 | 0.0084 | 0.6688 | | No log | 3.0 | 30 | 0.6139 | 0.0029 | 0.0241 | 0.0052 | 0.6659 | | No log | 4.0 | 40 | 0.6172 | 0.0020 | 0.0241 | 0.0037 | 0.6622 | | No log | 5.0 | 50 | 0.6165 | 0.0019 | 0.0241 | 0.0036 | 0.6599 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

DicoTiar/wisdomfiy

[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]

fill-mask

{ "architectures": [ "BertForMaskedLM" ], "model_type": "bert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12 results: [] ---  # distilBERT_token_itr0_0.0001_all_01_03_2022-15_22_12 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2811 - Precision: 0.3231 - Recall: 0.5151 - F1: 0.3971 - Accuracy: 0.8913 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.2881 | 0.2089 | 0.3621 | 0.2650 | 0.8715 | | No log | 2.0 | 60 | 0.2500 | 0.2619 | 0.3842 | 0.3115 | 0.8845 | | No log | 3.0 | 90 | 0.2571 | 0.2327 | 0.4338 | 0.3030 | 0.8809 | | No log | 4.0 | 120 | 0.2479 | 0.3051 | 0.4761 | 0.3719 | 0.8949 | | No log | 5.0 | 150 | 0.2783 | 0.3287 | 0.4761 | 0.3889 | 0.8936 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

DiegoBalam12/institute_classification

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_0.0001_essays_01_03_2022-15_18_35 results: [] ---  # distilBERT_token_itr0_0.0001_essays_01_03_2022-15_18_35 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1832 - Precision: 0.6138 - Recall: 0.7169 - F1: 0.6613 - Accuracy: 0.9332 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 11 | 0.2740 | 0.4554 | 0.5460 | 0.4966 | 0.8943 | | No log | 2.0 | 22 | 0.2189 | 0.5470 | 0.6558 | 0.5965 | 0.9193 | | No log | 3.0 | 33 | 0.2039 | 0.5256 | 0.6706 | 0.5893 | 0.9198 | | No log | 4.0 | 44 | 0.2097 | 0.5401 | 0.6795 | 0.6018 | 0.9237 | | No log | 5.0 | 55 | 0.2255 | 0.6117 | 0.6825 | 0.6452 | 0.9223 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Digakive/Hsgshs

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57 results: [] ---  # distilBERT_token_itr0_0.0001_webDiscourse_01_03_2022-15_16_57 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5923 - Precision: 0.0039 - Recall: 0.0212 - F1: 0.0066 - Accuracy: 0.7084 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.6673 | 0.0476 | 0.0128 | 0.0202 | 0.6652 | | No log | 2.0 | 20 | 0.6211 | 0.0 | 0.0 | 0.0 | 0.6707 | | No log | 3.0 | 30 | 0.6880 | 0.0038 | 0.0128 | 0.0058 | 0.6703 | | No log | 4.0 | 40 | 0.6566 | 0.0030 | 0.0128 | 0.0049 | 0.6690 | | No log | 5.0 | 50 | 0.6036 | 0.0 | 0.0 | 0.0 | 0.6868 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Dilmk2/DialoGPT-small-harrypotter

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

13

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_1e-05_all_01_03_2022-15_14_04 results: [] ---  # distilBERT_token_itr0_1e-05_all_01_03_2022-15_14_04 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3121 - Precision: 0.1204 - Recall: 0.2430 - F1: 0.1611 - Accuracy: 0.8538 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4480 | 0.0209 | 0.0223 | 0.0216 | 0.7794 | | No log | 2.0 | 60 | 0.3521 | 0.0559 | 0.1218 | 0.0767 | 0.8267 | | No log | 3.0 | 90 | 0.3177 | 0.1208 | 0.2504 | 0.1629 | 0.8487 | | No log | 4.0 | 120 | 0.3009 | 0.1296 | 0.2607 | 0.1731 | 0.8602 | | No log | 5.0 | 150 | 0.2988 | 0.1393 | 0.2693 | 0.1836 | 0.8599 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

DimaOrekhov/transformer-method-name

[ "pytorch", "encoder-decoder", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "EncoderDecoderModel" ], "model_type": "encoder-decoder", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

8

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_1e-05_essays_01_03_2022-15_11_44 results: [] ---  # distilBERT_token_itr0_1e-05_essays_01_03_2022-15_11_44 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3082 - Precision: 0.2796 - Recall: 0.4373 - F1: 0.3411 - Accuracy: 0.8887 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 11 | 0.5018 | 0.0192 | 0.0060 | 0.0091 | 0.7370 | | No log | 2.0 | 22 | 0.4066 | 0.1541 | 0.2814 | 0.1992 | 0.8340 | | No log | 3.0 | 33 | 0.3525 | 0.1768 | 0.3234 | 0.2286 | 0.8612 | | No log | 4.0 | 44 | 0.3250 | 0.2171 | 0.3503 | 0.2680 | 0.8766 | | No log | 5.0 | 55 | 0.3160 | 0.2353 | 0.3713 | 0.2880 | 0.8801 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Dimedrolza/DialoGPT-small-cyberpunk

[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]

conversational

{ "architectures": [ "GPT2LMHeadModel" ], "model_type": "gpt2", "task_specific_params": { "conversational": { "max_length": 1000 }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

9

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_10_39 results: [] ---  # distilBERT_token_itr0_1e-05_webDiscourse_01_03_2022-15_10_39 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5867 - Precision: 0.0119 - Recall: 0.0116 - F1: 0.0118 - Accuracy: 0.6976 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 10 | 0.5730 | 0.0952 | 0.0270 | 0.0421 | 0.7381 | | No log | 2.0 | 20 | 0.5755 | 0.0213 | 0.0135 | 0.0165 | 0.7388 | | No log | 3.0 | 30 | 0.5635 | 0.0196 | 0.0135 | 0.016 | 0.7416 | | No log | 4.0 | 40 | 0.5549 | 0.0392 | 0.0270 | 0.032 | 0.7429 | | No log | 5.0 | 50 | 0.5530 | 0.0357 | 0.0270 | 0.0308 | 0.7438 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Dmitry12/sber

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert_token_itr0_1e-05_all_01_03_2022-14_33_33 results: [] ---  # distilbert_token_itr0_1e-05_all_01_03_2022-14_33_33 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3255 - Precision: 0.1412 - Recall: 0.25 - F1: 0.1805 - Accuracy: 0.8491 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4549 | 0.0228 | 0.0351 | 0.0276 | 0.7734 | | No log | 2.0 | 60 | 0.3577 | 0.0814 | 0.1260 | 0.0989 | 0.8355 | | No log | 3.0 | 90 | 0.3116 | 0.1534 | 0.2648 | 0.1943 | 0.8611 | | No log | 4.0 | 120 | 0.2975 | 0.1792 | 0.2967 | 0.2234 | 0.8690 | | No log | 5.0 | 150 | 0.2935 | 0.1873 | 0.2998 | 0.2305 | 0.8715 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

DongHyoungLee/distilbert-base-uncased-finetuned-cola

[ "pytorch", "tensorboard", "distilbert", "text-classification", "dataset:glue", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]

text-classification

{ "architectures": [ "DistilBertForSequenceClassification" ], "model_type": "distilbert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

27

"2022-02-27T18:11:24Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 results: [] ---  # finetuned_sentence_itr0_0.0002_all_27_02_2022-19_11_17 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4064 - Accuracy: 0.8289 - F1: 0.8901 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.4163 | 0.8085 | 0.8780 | | No log | 2.0 | 390 | 0.4098 | 0.8268 | 0.8878 | | 0.312 | 3.0 | 585 | 0.5892 | 0.8244 | 0.8861 | | 0.312 | 4.0 | 780 | 0.7580 | 0.8232 | 0.8845 | | 0.312 | 5.0 | 975 | 0.9028 | 0.8183 | 0.8824 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Donghyun/L2_BERT

[]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

0

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 results: [] ---  # finetuned_sentence_itr0_0.0002_editorials_27_02_2022-19_42_36 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0926 - Accuracy: 0.9772 - F1: 0.9883 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 104 | 0.0539 | 0.9885 | 0.9942 | | No log | 2.0 | 208 | 0.0282 | 0.9885 | 0.9942 | | No log | 3.0 | 312 | 0.0317 | 0.9914 | 0.9956 | | No log | 4.0 | 416 | 0.0462 | 0.9885 | 0.9942 | | 0.0409 | 5.0 | 520 | 0.0517 | 0.9885 | 0.9942 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Dongjae/mrc2reader

[ "pytorch", "xlm-roberta", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "XLMRobertaForQuestionAnswering" ], "model_type": "xlm-roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 results: [] ---  # finetuned_sentence_itr0_0.0002_essays_27_02_2022-19_33_10 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3358 - Accuracy: 0.8688 - F1: 0.9225 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 81 | 0.4116 | 0.8382 | 0.9027 | | No log | 2.0 | 162 | 0.4360 | 0.8382 | 0.8952 | | No log | 3.0 | 243 | 0.5719 | 0.8382 | 0.8995 | | No log | 4.0 | 324 | 0.7251 | 0.8493 | 0.9021 | | No log | 5.0 | 405 | 0.8384 | 0.8456 | 0.9019 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Dongmin/testmodel

[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "T5ForConditionalGeneration" ], "model_type": "t5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": true, "length_penalty": 2, "max_length": 200, "min_length": 30, "no_repeat_ngram_size": 3, "num_beams": 4, "prefix": "summarize: " }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to German: " }, "translation_en_to_fr": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to French: " }, "translation_en_to_ro": { "early_stopping": true, "max_length": 300, "num_beams": 4, "prefix": "translate English to Romanian: " } } }

11

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 results: [] ---  # finetuned_sentence_itr0_0.0002_webDiscourse_27_02_2022-19_25_06 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5777 - Accuracy: 0.6794 - F1: 0.5010 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 48 | 0.6059 | 0.63 | 0.4932 | | No log | 2.0 | 96 | 0.6327 | 0.705 | 0.5630 | | No log | 3.0 | 144 | 0.7003 | 0.695 | 0.5197 | | No log | 4.0 | 192 | 0.9368 | 0.69 | 0.4655 | | No log | 5.0 | 240 | 1.1935 | 0.685 | 0.4425 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Waynehillsdev/Wayne_NLP_mT5

[ "pytorch", "tensorboard", "mt5", "text2text-generation", "dataset:cnn_dailymail", "transformers", "generated_from_trainer", "autotrain_compatible" ]

text2text-generation

{ "architectures": [ "MT5ForConditionalGeneration" ], "model_type": "mt5", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

11

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 results: [] ---  # finetuned_sentence_itr0_1e-05_all_01_03_2022-13_25_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4787 - Accuracy: 0.8138 - F1: 0.8785 - Precision: 0.8489 - Recall: 0.9101 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 390 | 0.4335 | 0.7732 | 0.8533 | 0.8209 | 0.8883 | | 0.5141 | 2.0 | 780 | 0.4196 | 0.8037 | 0.8721 | 0.8446 | 0.9015 | | 0.3368 | 3.0 | 1170 | 0.4519 | 0.8098 | 0.8779 | 0.8386 | 0.9212 | | 0.2677 | 4.0 | 1560 | 0.4787 | 0.8122 | 0.8785 | 0.8452 | 0.9146 | | 0.2677 | 5.0 | 1950 | 0.4912 | 0.8146 | 0.8794 | 0.8510 | 0.9097 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Waynehillsdev/wav2vec2-base-timit-demo-colab

[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0" ]

automatic-speech-recognition

{ "architectures": [ "Wav2Vec2ForCTC" ], "model_type": "wav2vec2", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

5

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 results: [] ---  # finetuned_sentence_itr0_2e-05_all_01_03_2022-13_11_55 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6168 - Accuracy: 0.8286 - F1: 0.8887 - Precision: 0.8628 - Recall: 0.9162 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 390 | 0.3890 | 0.8110 | 0.8749 | 0.8631 | 0.8871 | | 0.4535 | 2.0 | 780 | 0.3921 | 0.8439 | 0.8984 | 0.8721 | 0.9264 | | 0.266 | 3.0 | 1170 | 0.4454 | 0.8415 | 0.8947 | 0.8860 | 0.9034 | | 0.16 | 4.0 | 1560 | 0.5610 | 0.8427 | 0.8957 | 0.8850 | 0.9067 | | 0.16 | 5.0 | 1950 | 0.6180 | 0.8488 | 0.9010 | 0.8799 | 0.9231 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Doohae/p_encoder

[ "pytorch" ]

null

{ "architectures": null, "model_type": null, "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 results: [] ---  # finetuned_sentence_itr0_2e-05_all_26_02_2022-03_57_45 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4345 - Accuracy: 0.8321 - F1: 0.8904 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3922 | 0.8061 | 0.8747 | | No log | 2.0 | 390 | 0.3764 | 0.8171 | 0.8837 | | 0.4074 | 3.0 | 585 | 0.3873 | 0.8220 | 0.8843 | | 0.4074 | 4.0 | 780 | 0.4361 | 0.8232 | 0.8854 | | 0.4074 | 5.0 | 975 | 0.4555 | 0.8159 | 0.8793 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

Doohae/roberta

[ "pytorch", "roberta", "question-answering", "transformers", "autotrain_compatible" ]

question-answering

{ "architectures": [ "RobertaForQuestionAnswering" ], "model_type": "roberta", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

3

null

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 results: [] ---  # finetuned_sentence_itr0_2e-05_all_27_02_2022-19_05_42 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4917 - Accuracy: 0.8231 - F1: 0.8833 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3883 | 0.8146 | 0.8833 | | No log | 2.0 | 390 | 0.3607 | 0.8390 | 0.8964 | | 0.4085 | 3.0 | 585 | 0.3812 | 0.8488 | 0.9042 | | 0.4085 | 4.0 | 780 | 0.3977 | 0.8549 | 0.9077 | | 0.4085 | 5.0 | 975 | 0.4233 | 0.8573 | 0.9092 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

albert-base-v1

[ "pytorch", "tf", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "exbert", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

38,156

"2022-02-27T17:07:05Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59 results: [] ---  # finetuned_sentence_itr2_0.0002_all_27_02_2022-18_06_59 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7600 - Accuracy: 0.8144 - F1: 0.8788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3514 | 0.8427 | 0.8979 | | No log | 2.0 | 390 | 0.3853 | 0.8293 | 0.8936 | | 0.3147 | 3.0 | 585 | 0.5494 | 0.8268 | 0.8868 | | 0.3147 | 4.0 | 780 | 0.6235 | 0.8427 | 0.8995 | | 0.3147 | 5.0 | 975 | 0.8302 | 0.8378 | 0.8965 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

albert-base-v2

[ "pytorch", "tf", "jax", "rust", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

4,785,283

"2022-02-26T03:09:07Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 results: [] ---  # finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4676 - Accuracy: 0.8299 - F1: 0.8892 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.4087 | 0.8073 | 0.8754 | | No log | 2.0 | 390 | 0.3952 | 0.8159 | 0.8803 | | 0.4084 | 3.0 | 585 | 0.4183 | 0.8195 | 0.8831 | | 0.4084 | 4.0 | 780 | 0.4596 | 0.8280 | 0.8867 | | 0.4084 | 5.0 | 975 | 0.4919 | 0.8280 | 0.8873 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

albert-large-v1

[ "pytorch", "tf", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

687

"2022-02-27T16:39:04Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 results: [] ---  # finetuned_sentence_itr2_2e-05_all_27_02_2022-17_38_58 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4095 - Accuracy: 0.8263 - F1: 0.8865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3685 | 0.8293 | 0.8911 | | No log | 2.0 | 390 | 0.3495 | 0.8415 | 0.8992 | | 0.4065 | 3.0 | 585 | 0.3744 | 0.8463 | 0.9014 | | 0.4065 | 4.0 | 780 | 0.4260 | 0.8427 | 0.8980 | | 0.4065 | 5.0 | 975 | 0.4548 | 0.8366 | 0.8940 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

albert-large-v2

[ "pytorch", "tf", "safetensors", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

26,792

"2022-02-27T17:56:36Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32 results: [] ---  # finetuned_sentence_itr2_2e-05_webDiscourse_27_02_2022-18_56_32 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6049 - Accuracy: 0.6926 - F1: 0.4160 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 48 | 0.5835 | 0.71 | 0.0333 | | No log | 2.0 | 96 | 0.5718 | 0.715 | 0.3871 | | No log | 3.0 | 144 | 0.5731 | 0.715 | 0.4 | | No log | 4.0 | 192 | 0.6009 | 0.705 | 0.3516 | | No log | 5.0 | 240 | 0.6122 | 0.7 | 0.4000 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

albert-xlarge-v1

[ "pytorch", "tf", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

341

"2022-02-27T17:35:08Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02 results: [] ---  # finetuned_sentence_itr2_3e-05_all_27_02_2022-18_35_02 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3962 - Accuracy: 0.8231 - F1: 0.8873 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3591 | 0.8366 | 0.8950 | | No log | 2.0 | 390 | 0.3558 | 0.8415 | 0.9012 | | 0.3647 | 3.0 | 585 | 0.4049 | 0.8427 | 0.8983 | | 0.3647 | 4.0 | 780 | 0.5030 | 0.8378 | 0.8949 | | 0.3647 | 5.0 | 975 | 0.5719 | 0.8354 | 0.8943 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3

albert-xlarge-v2

[ "pytorch", "tf", "albert", "fill-mask", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1909.11942", "transformers", "license:apache-2.0", "autotrain_compatible", "has_space" ]

fill-mask

{ "architectures": [ "AlbertForMaskedLM" ], "model_type": "albert", "task_specific_params": { "conversational": { "max_length": null }, "summarization": { "early_stopping": null, "length_penalty": null, "max_length": null, "min_length": null, "no_repeat_ngram_size": null, "num_beams": null, "prefix": null }, "text-generation": { "do_sample": null, "max_length": null }, "translation_en_to_de": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_fr": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null }, "translation_en_to_ro": { "early_stopping": null, "max_length": null, "num_beams": null, "prefix": null } } }

2,973

"2022-02-27T17:12:41Z"

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34 results: [] ---  # finetuned_sentence_itr3_0.0002_all_27_02_2022-18_12_34 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7600 - Accuracy: 0.8144 - F1: 0.8788 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 195 | 0.3514 | 0.8427 | 0.8979 | | No log | 2.0 | 390 | 0.3853 | 0.8293 | 0.8936 | | 0.3147 | 3.0 | 585 | 0.5494 | 0.8268 | 0.8868 | | 0.3147 | 4.0 | 780 | 0.6235 | 0.8427 | 0.8995 | | 0.3147 | 5.0 | 975 | 0.8302 | 0.8378 | 0.8965 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3