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--- |
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language: fr |
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license: mit |
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datasets: |
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- oscar |
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--- |
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# CamemBERT: a Tasty French Language Model |
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## Introduction |
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[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. |
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It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. |
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For further information or requests, please go to [Camembert Website](https://camembert-model.fr/) |
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## Pre-trained models |
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| Model | #params | Arch. | Training data | |
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|--------------------------------|--------------------------------|-------|-----------------------------------| |
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| `camembert-base` | 110M | Base | OSCAR (138 GB of text) | |
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| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) | |
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| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) | |
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| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) | |
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| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) | |
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| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) | |
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## How to use CamemBERT with HuggingFace |
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##### Load CamemBERT and its sub-word tokenizer : |
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```python |
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from transformers import CamembertModel, CamembertTokenizer |
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# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large". |
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tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-wikipedia-4gb") |
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camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb") |
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camembert.eval() # disable dropout (or leave in train mode to finetune) |
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``` |
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##### Filling masks using pipeline |
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```python |
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from transformers import pipeline |
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camembert_fill_mask = pipeline("fill-mask", model="camembert/camembert-base-wikipedia-4gb", tokenizer="camembert/camembert-base-wikipedia-4gb") |
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results = camembert_fill_mask("Le camembert est un fromage de <mask>!") |
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# results |
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#[{'sequence': '<s> Le camembert est un fromage de chèvre!</s>', 'score': 0.4937814474105835, 'token': 19370}, |
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#{'sequence': '<s> Le camembert est un fromage de brebis!</s>', 'score': 0.06255942583084106, 'token': 30616}, |
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#{'sequence': '<s> Le camembert est un fromage de montagne!</s>', 'score': 0.04340197145938873, 'token': 2364}, |
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# {'sequence': '<s> Le camembert est un fromage de Noël!</s>', 'score': 0.02823255956172943, 'token': 3236}, |
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#{'sequence': '<s> Le camembert est un fromage de vache!</s>', 'score': 0.021357402205467224, 'token': 12329}] |
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``` |
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##### Extract contextual embedding features from Camembert output |
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```python |
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import torch |
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# Tokenize in sub-words with SentencePiece |
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tokenized_sentence = tokenizer.tokenize("J'aime le camembert !") |
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# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] |
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# 1-hot encode and add special starting and end tokens |
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encoded_sentence = tokenizer.encode(tokenized_sentence) |
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# [5, 221, 10, 10600, 14, 8952, 10540, 75, 1114, 6] |
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# NB: Can be done in one step : tokenize.encode("J'aime le camembert !") |
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# Feed tokens to Camembert as a torch tensor (batch dim 1) |
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encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0) |
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embeddings, _ = camembert(encoded_sentence) |
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# embeddings.detach() |
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# embeddings.size torch.Size([1, 10, 768]) |
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#tensor([[[-0.0928, 0.0506, -0.0094, ..., -0.2388, 0.1177, -0.1302], |
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# [ 0.0662, 0.1030, -0.2355, ..., -0.4224, -0.0574, -0.2802], |
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# [-0.0729, 0.0547, 0.0192, ..., -0.1743, 0.0998, -0.2677], |
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# ..., |
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``` |
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##### Extract contextual embedding features from all Camembert layers |
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```python |
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from transformers import CamembertConfig |
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# (Need to reload the model with new config) |
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config = CamembertConfig.from_pretrained("camembert/camembert-base-wikipedia-4gb", output_hidden_states=True) |
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camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb", config=config) |
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embeddings, _, all_layer_embeddings = camembert(encoded_sentence) |
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# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers) |
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all_layer_embeddings[5] |
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# layer 5 contextual embedding : size torch.Size([1, 10, 768]) |
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#tensor([[[-0.0059, -0.0227, 0.0065, ..., -0.0770, 0.0369, 0.0095], |
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# [ 0.2838, -0.1531, -0.3642, ..., -0.0027, -0.8502, -0.7914], |
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# [-0.0073, -0.0338, -0.0011, ..., 0.0533, -0.0250, -0.0061], |
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# ..., |
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``` |
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## Authors |
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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. |
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## Citation |
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If you use our work, please cite: |
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```bibtex |
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@inproceedings{martin2020camembert, |
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title={CamemBERT: a Tasty French Language Model}, |
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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}, |
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booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, |
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year={2020} |
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} |
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``` |
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