readme: ---
  language: fr
  ---
  
  # CamemBERT: a Tasty French Language Model
  
  ## Introduction
  
  [CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model. 
  
  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. 
  
  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
  
  # You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
  tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-wikipedia-4gb")
  camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb")
  
  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/camembert-base-wikipedia-4gb", tokenizer="camembert/camembert-base-wikipedia-4gb")
  results = camembert_fill_mask("Le camembert est un fromage de <mask>!")
  # results
  #[{'sequence': '<s> Le camembert est un fromage de chèvre!</s>', 'score': 0.4937814474105835, 'token': 19370}, 
  #{'sequence': '<s> Le camembert est un fromage de brebis!</s>', 'score': 0.06255942583084106, 'token': 30616}, 
  #{'sequence': '<s> Le camembert est un fromage de montagne!</s>', 'score': 0.04340197145938873, 'token': 2364},
  # {'sequence': '<s> Le camembert est un fromage de Noël!</s>', 'score': 0.02823255956172943, 'token': 3236}, 
  #{'sequence': '<s> Le camembert est un fromage de vache!</s>', 'score': 0.021357402205467224, 'token': 12329}]
  ```
  
  ##### 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, 221, 10, 10600, 14, 8952, 10540, 75, 1114, 6]
  # NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
  
  # Feed tokens 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([[[-0.0928,  0.0506, -0.0094,  ..., -0.2388,  0.1177, -0.1302],
  #         [ 0.0662,  0.1030, -0.2355,  ..., -0.4224, -0.0574, -0.2802],
  #         [-0.0729,  0.0547,  0.0192,  ..., -0.1743,  0.0998, -0.2677],
  #         ...,
  ```
  
  ##### 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/camembert-base-wikipedia-4gb", output_hidden_states=True)
  camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb", 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([[[-0.0059, -0.0227,  0.0065,  ..., -0.0770,  0.0369,  0.0095],
  #         [ 0.2838, -0.1531, -0.3642,  ..., -0.0027, -0.8502, -0.7914],
  #         [-0.0073, -0.0338, -0.0011,  ...,  0.0533, -0.0250, -0.0061],
  #         ...,
  ```
  
  
  ## 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 an