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CamemBERT: a Tasty French Language Model

Table of Contents

Model Details

  • Model Description: CamemBERT 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.
  • Developed by: Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
  • Model Type: Fill-Mask
  • Language(s): French
  • License: MIT
  • Parent Model: See the RoBERTa base model for more information about the RoBERTa base model.
  • Resources for more information:

Uses

Direct Use

This model can be used for Fill-Mask tasks.

Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).

This model was pretrinaed on a subcorpus of OSCAR multilingual corpus. Some of the limitations and risks associated with the OSCAR dataset, which are further detailed in the OSCAR dataset card, include the following:

The quality of some OSCAR sub-corpora might be lower than expected, specifically for the lowest-resource languages.

Constructed from Common Crawl, Personal and sensitive information might be present.

Training

Training Data

OSCAR or Open Super-large Crawled Aggregated coRpus is a multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture.

Training Procedure

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)

Evaluation

The model developers evaluated CamemBERT using four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI).

Citation Information

@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}
}

How to Get Started With the Model

Load CamemBERT and its sub-word tokenizer :
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
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
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
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],
#         ...,
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Dataset used to train camembert-base

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