Qanastek

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Number of Runs: 6,638

Models by this creator

51-languages-classifier

51-languages-classifier

qanastek

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5.0K

Huggingface

pos-french-camembert

pos-french-camembert

POET: A French Extended Part-of-Speech Tagger Corpora: ANTILLES Embeddings & Sequence Labelling: CamemBERT Number of Epochs: 115 People Involved LABRAK Yanis (1) DUFOUR Richard (2) Affiliations LIA, NLP team, Avignon University, Avignon, France. LS2N, TALN team, Nantes University, Nantes, France. Demo: How to use in HuggingFace Transformers Requires transformers: pip install transformers Output: Training data ANTILLES is a part-of-speech tagging corpora based on UD_French-GSD which was originally created in 2015 and is based on the universal dependency treebank v2.0. Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora. We based our tags on the level of details given by the LIA_TAGG statistical POS tagger written by Frédéric Béchet in 2001. The corpora used for this model is available on Github at the CoNLL-U format. Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive. Original Tags New additional POS tags Evaluation results The test corpora used for this evaluation is available on Github. BibTeX Citations Please cite the following paper when using this model. ANTILLES corpus and POET taggers: UD_French-GSD corpora: LIA TAGG: Flair Embeddings: Acknowledgment This work was financially supported by Zenidoc

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374

Huggingface

FrenchMedMCQA-BART-base-Wikipedia-BM25

FrenchMedMCQA-BART-base-Wikipedia-BM25

FrenchMedMCQA : Multiple-choice question answering on pharmacology exams using BART-base, Wikipedia external knowledge and BM25 retriever Corpora: FrenchMedMCQA Model: BART Base Number of Epochs: 30 People Involved Yanis LABRAK (1) Adrien BAZOGE (2) Richard DUFOUR (2) BĂ©atrice DAILLE (2) Pierre-Antoine GOURRAUD (3) Emmanuel MORIN (2) Mickael ROUVIER (1) Affiliations LIA, NLP team, Avignon University, Avignon, France. LS2N, TALN team, Nantes University, Nantes, France. CHU Nantes, Nantes University, Nantes, France. Demo: How to use in HuggingFace Transformers Requires Transformers: pip install transformers Output: Training data The questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers. Evaluation results The test corpora used for this evaluation is available on Github. BibTeX Citations Please cite the following paper when using this model. FrenchMedMCQA corpus and linked tools: HuggingFace's Transformers : Acknowledgment This work was financially supported by Zenidoc, the DIETS project financed by the Agence Nationale de la Recherche (ANR) under contract ANR-20-CE23-0005 and the ANR AIBy4 (ANR-20-THIA-0011).

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71

Huggingface

FrenchMedMCQA-BioBERT-V1.1-Wikipedia-BM25

FrenchMedMCQA-BioBERT-V1.1-Wikipedia-BM25

FrenchMedMCQA : Multiple-choice question answering on pharmacology exams using BioBERT V1.1, Wikipedia external knowledge and BM25 retriever Corpora: FrenchMedMCQA Model: BioBERT V1.1 Number of Epochs: 10 People Involved Yanis LABRAK (1) Adrien BAZOGE (2) Richard DUFOUR (2) BĂ©atrice DAILLE (2) Pierre-Antoine GOURRAUD (3) Emmanuel MORIN (2) Mickael ROUVIER (1) Affiliations LIA, NLP team, Avignon University, Avignon, France. LS2N, TALN team, Nantes University, Nantes, France. CHU Nantes, Nantes University, Nantes, France. Demo: How to use in HuggingFace Transformers Requires Transformers: pip install transformers Output: Training data The questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers. Evaluation results The test corpora used for this evaluation is available on Github. BibTeX Citations Please cite the following paper when using this model. FrenchMedMCQA corpus and linked tools: HuggingFace's Transformers : Acknowledgment This work was financially supported by Zenidoc, the DIETS project financed by the Agence Nationale de la Recherche (ANR) under contract ANR-20-CE23-0005 and the ANR AIBy4 (ANR-20-THIA-0011).

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32

Huggingface

whisper-tiny-french-cased

whisper-tiny-french-cased

Whisper Tiny French Cased This model is a fine-tuned version of openai/whisper-tiny on the mozilla-foundation/common_voice_11_0 fr dataset. It achieves the following results on the evaluation set: Loss: 0.6509 Wer on mozilla-foundation/common_voice_11_0 fr: 33.0655 Wer on google/fleurs fr_fr: 36.69 Wer on facebook/voxpopuli fr: 32.71 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: 16 seed: 42 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 lr_scheduler_type: linear lr_scheduler_warmup_steps: 500 training_steps: 5000 mixed_precision_training: Native AMP Training results Framework versions Transformers 4.26.0.dev0 Pytorch 1.11.0+cu102 Datasets 2.7.1.dev0 Tokenizers 0.13.2

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27

Huggingface

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