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Read%20announcement%20at%20https%3A//discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755%0AOriginal%20file%20history%3A%20https%3A//github.com/huggingface/transformers/commits/master/model_cards/camembert/camembert-base-wikipedia-4gb/README.md

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+ ---
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+ language: fr
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+ ---
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+
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+ # CamemBERT: a Tasty French Language Model
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+
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+ ## Introduction
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+
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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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+
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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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+
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+ For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
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+
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+ ## Pre-trained models
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+
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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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+
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+ ## How to use CamemBERT with HuggingFace
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+
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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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+
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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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+
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+ camembert.eval() # disable dropout (or leave in train mode to finetune)
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+
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+ ```
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+
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+ ## Authors
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+
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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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+
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+
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+ ## Citation
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+ If you use our work, please cite:
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+
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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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+ ```