From 73efa694e6cc9141ce732ea8215b25b3083298dc Mon Sep 17 00:00:00 2001 From: Benjamin Muller Date: Sat, 18 Apr 2020 08:08:13 +0800 Subject: [PATCH] Update camembert-base-README.md (#3836) --- model_cards/camembert-base-README.md | 108 +++++++++++++++++++++++++-- 1 file changed, 103 insertions(+), 5 deletions(-) diff --git a/model_cards/camembert-base-README.md b/model_cards/camembert-base-README.md index 3389052c05..2cc14119c5 100644 --- a/model_cards/camembert-base-README.md +++ b/model_cards/camembert-base-README.md @@ -2,12 +2,110 @@ language: french --- -# CamemBERT +# CamemBERT: a Tasty French Language Model -CamemBERT is a state-of-the-art language model for French based on the RoBERTa architecture pretrained on the French subcorpus of the newly available multilingual corpus OSCAR. +## Introduction -CamemBERT was originally evaluated on four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI); improving the state of the art for most tasks over previous monolingual and multilingual approaches, which confirms the effectiveness of large pretrained language models for French. +[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa architecture. -CamemBERT was trained and evaluated by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. +It is now available on Hugging Face in 6 different versions varying the number of parameters, the amount of pretraining data and the pretraining data source domains. + +For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) + +## Pre-trained models + +| Model | #params | Arch. | Training data | +|--------------------------------|---------|--------------------------------------------------------------------------------------------------------------------------|-------|-----------------------------------| +| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | +| `camembert` / `camembert-large` | 335M | Large | CCNet (135 GB of text) | +| `camembert` / `camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | +| `camembert` / `camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | +| `camembert` / `camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | +| `camembert` / `camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | + +## How to use CamemBERT with HuggingFace + +##### Load CamemBERT and its sub-word tokenizer : +```python +from transformers import CamembertModel, CamembertTokenizer + +tokenizer = CamembertTokenizer.from_pretrained("camembert-base") +camembert = CamembertModel.from_pretrained("camembert-base") + +camembert.eval() # disable dropout (or leave in train mode to finetune) + +``` + +##### Filling masks using pipeline +```python +from transformers import pipeline + +camembert_fill_mask = pipeline("fill-mask",model="camembert-base",tokenizer="camembert-base") +results = camembert_fill_mask("Le camembert est :)") +# results +#[{'sequence': ' Le camembert est délicieux :)', 'score': 0.4909103214740753, 'token': 7200}, +# {'sequence': ' Le camembert est excellent :)', 'score': 0.10556930303573608, 'token': 2183}, +# {'sequence': ' Le camembert est succulent :)', 'score': 0.03453315049409866, 'token': 26202}, +# {'sequence': ' Le camembert est meilleur :)', 'score': 0.03303130343556404, 'token': 528}, +# {'sequence': ' Le camembert est parfait :)', 'score': 0.030076518654823303, 'token': 1654}] + +``` + +##### Extract contextual embedding features from Camembert output +```python +import torch +# Tokenize in sub-words with SentencePiece +tokenized_sentence = tokenizer.tokenize("J'aime le camembert !") +# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] + +# 1-hot encode and add special starting and end tokens +encoded_sentence = tokenizer.encode(tokenized_sentence) +# [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6] +# NB: can do in one step : tokenize.encode("J'aime le camembert !") + +# Feed to Camembert as a torch tensor (batch dim 1) +encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0) +embeddings, _ = camembert(encoded_sentence) +# embeddings.detach() +# embeddings.size torch.Size([1, 10, 768]) +# tensor([[[-0.0254, 0.0235, 0.1027, ..., -0.1459, -0.0205, -0.0116], +# [ 0.0606, -0.1811, -0.0418, ..., -0.1815, 0.0880, -0.0766], +# [-0.1561, -0.1127, 0.2687, ..., -0.0648, 0.0249, 0.0446], +# ..., +``` + +##### Extract contextual embedding features from all Camembert layers +```python +from transformers import CamembertConfig +# (Need to reload the model with new config) +config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True) +camembert = CamembertModel.from_pretrained("camembert-base",config=config) + +embeddings, _, all_layer_embeddings = camembert(encoded_sentence) +# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers) +all_layer_embeddings[5] +# layer 5 contextual embedding : size torch.Size([1, 10, 768]) +#tensor([[[-0.0032, 0.0075, 0.0040, ..., -0.0025, -0.0178, -0.0210], +# [-0.0996, -0.1474, 0.1057, ..., -0.0278, 0.1690, -0.2982], +# [ 0.0557, -0.0588, 0.0547, ..., -0.0726, -0.0867, 0.0699], +# ..., +``` + + +## Authors + +CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot. + + +## Citation +If you use our work, please cite: + +```bibtex +@inproceedings{martin2020camembert, + title={CamemBERT: a Tasty French Language Model}, + author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t}, + booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, + year={2020} +} +``` -Preprint can be found [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894)