| # CamemBERT: a Tasty French Language Model |
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| ## Introduction |
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| [CamemBERT](https://arxiv.org/abs/1911.03894) is a pretrained language model trained on 138GB of French text based on RoBERTa. |
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| Also available in [github.com/huggingface/transformers](https://github.com/huggingface/transformers/). |
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| ## Pre-trained models |
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| | Model | #params | Download | Arch. | Training data | |
| |--------------------------------|---------|--------------------------------------------------------------------------------------------------------------------------|-------|-----------------------------------| |
| | `camembert` / `camembert-base` | 110M | [camembert-base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz) | Base | OSCAR (138 GB of text) | |
| | `camembert-large` | 335M | [camembert-large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-large.tar.gz) | Large | CCNet (135 GB of text) | |
| | `camembert-base-ccnet` | 110M | [camembert-base-ccnet.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet.tar.gz) | Base | CCNet (135 GB of text) | |
| | `camembert-base-wikipedia-4gb` | 110M | [camembert-base-wikipedia-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-wikipedia-4gb.tar.gz) | Base | Wikipedia (4 GB of text) | |
| | `camembert-base-oscar-4gb` | 110M | [camembert-base-oscar-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-oscar-4gb.tar.gz) | Base | Subsample of OSCAR (4 GB of text) | |
| | `camembert-base-ccnet-4gb` | 110M | [camembert-base-ccnet-4gb.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/camembert-base-ccnet-4gb.tar.gz) | Base | Subsample of CCNet (4 GB of text) | |
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| ## Example usage |
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| ### fairseq |
| ##### Load CamemBERT from torch.hub (PyTorch >= 1.1): |
| ```python |
| import torch |
| camembert = torch.hub.load('pytorch/fairseq', 'camembert') |
| camembert.eval() # disable dropout (or leave in train mode to finetune) |
| ``` |
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| ##### Load CamemBERT (for PyTorch 1.0 or custom models): |
| ```python |
| # Download camembert model |
| wget https://dl.fbaipublicfiles.com/fairseq/models/camembert-base.tar.gz |
| tar -xzvf camembert.tar.gz |
| |
| # Load the model in fairseq |
| from fairseq.models.roberta import CamembertModel |
| camembert = CamembertModel.from_pretrained('/path/to/camembert') |
| camembert.eval() # disable dropout (or leave in train mode to finetune) |
| ``` |
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| ##### Filling masks: |
| ```python |
| masked_line = 'Le camembert est <mask> :)' |
| camembert.fill_mask(masked_line, topk=3) |
| # [('Le camembert est délicieux :)', 0.4909118115901947, ' délicieux'), |
| # ('Le camembert est excellent :)', 0.10556942224502563, ' excellent'), |
| # ('Le camembert est succulent :)', 0.03453322499990463, ' succulent')] |
| ``` |
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| ##### Extract features from Camembert: |
| ```python |
| # Extract the last layer's features |
| line = "J'aime le camembert !" |
| tokens = camembert.encode(line) |
| last_layer_features = camembert.extract_features(tokens) |
| assert last_layer_features.size() == torch.Size([1, 10, 768]) |
| |
| # Extract all layer's features (layer 0 is the embedding layer) |
| all_layers = camembert.extract_features(tokens, return_all_hiddens=True) |
| assert len(all_layers) == 13 |
| assert torch.all(all_layers[-1] == last_layer_features) |
| ``` |
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|
| ## Citation |
| If you use our work, please cite: |
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| ```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} |
| } |
| ``` |
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