readme: ---
  language: fr
  license: mit
  datasets:
  - oscar
  ---
  
  # CamemBERT: a Tasty French Language Model
  
  **This model is a copy of [this model repository](https://huggingface.co/camembert-base) at the specific commit `482393b6198924f9da270b1aaf37d238aafca99b`.**
  
  ## 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-base")
  camembert = CamembertModel.from_pretrained("camembert-base")
  
  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-base", tokenizer="camembert-base")
  results = camembert_fill_mask("Le camembert est <mask> :)")
  # results
  #[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.4909103214740753, 'token': 7200},
  # {'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.10556930303573608, 'token': 2183}, 
  # {'sequence': '<s> Le camembert est succulent :)</s>', 'score': 0.03453315049409866, 'token': 26202}, 
  # {'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.03303130343556404, 'token': 528}, 
  # {'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.030076518654823303, 'token': 1654}]
  
  ```
  
  ##### 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, 121, 11, 660, 16, 730, 25543, 110, 83, 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([[[-0.0254,  0.0235,  0.1027,  ..., -0.1459, -0.0205, -0.0116],
  #         [ 0.0606, -0.1811, -0.0418,  ..., -0.1815,  0.0880, -0.0766],
  #         [-0.1561, -0.1127,  0.2687,  ..., -0.0648,  0.0249,  0.0446],
  #         ...,
  ```
  
  ##### 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-base", output_hidden_states=True)
  camembert = CamembertModel.from_pretrained("camembert-base", 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([[[-0.0032,  0.0075,  0.0040,  ..., -0.0025, -0.0178, -0.0210],
  #         [-0.0996, -0.1474,  0.1057,  ..., -0.0278,  0.1690, -0.2982],
  #         [ 0.0557, -0.0588,  0.0547,  ..., -0.0726, -0.0867,  0.0699],
  #         ...,
  ```
  
  
  ## 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 Clergeri