metadata
language: fr
license: mit
datasets:
- oscar
CamemBERT: a Tasty French Language Model
This model is a copy of this model repository at the specific commit 482393b6198924f9da270b1aaf37d238aafca99b.
Introduction
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.
For further information or requests, please go to Camembert Website
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 :
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],
# ...,
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:
@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}
}