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-large")
  camembert = CamembertModel.from_pretrained("camembert/camembert-large")
  
  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-large", tokenizer="camembert/camembert-large")
  results = camembert_fill_mask("Le camembert est <mask> :)")
  # results
  #[{'sequence': '<s> Le camembert est bon :)</s>', 'score': 0.15560828149318695, 'token': 305}, 
  #{'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.06821336597204208, 'token': 3497}, 
  #{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.060438305139541626, 'token': 11661}, 
  #{'sequence': '<s> Le camembert est ici :)</s>', 'score': 0.02023460529744625, 'token': 373}, 
  #{'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.01778135634958744, 'token': 876}]
  ```
  
  ##### 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', '▁cam', 'ember', 't', '▁!']
  
  # 1-hot encode and add special starting and end tokens 
  encoded_sentence = tokenizer.encode(tokenized_sentence)
  # [5, 133, 22, 1250, 16, 12034, 14324, 81, 76, 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()
  # torch.Size([1, 10, 1024])
  #tensor([[[-0.1284,  0.2643,  0.4374,  ...,  0.1627,  0.1308, -0.2305],
  #         [ 0.4576, -0.6345, -0.2029,  ..., -0.1359, -0.2290, -0.6318],
  #         [ 0.0381,  0.0429,  0.5111,  ..., -0.1177, -0.1913, -0.1121],
  #         ...,
  ```
  
  ##### 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-large", output_hidden_states=True)
  camembert = CamembertModel.from_pretrained("camembert/camembert-large", config=config)
  
  embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
  #  all_layer_embeddings list of len(all_layer_embeddings) == 25 (input embedding layer + 24 self attention layers)
  all_layer_embeddings[5]
  # layer 5 contextual embedding : size torch.Size([1, 10, 1024])
  #tensor([[[-0.0600,  0.0742,  0.0332,  ..., -0.0525, -0.0637, -0.0287],
  #         [ 0.0950,  0.2840,  0.1985,  ...,  0.2073, -0.2172, -0.6321],
  #         [ 0.1381,  0.1872,  0.1614,  ..., -0.0339, -0.2530, -0.1182],
  #         ...,
  ```
  
  
  ## 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 Computa