Flaubert Small Cased

🚀 FlauBERT: Unsupervised Language Model Pre-training for French

FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Different-sized models are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer.

Along with FlauBERT comes FLUE, an evaluation setup for French NLP systems similar to the popular GLUE benchmark. The goal is to enable further reproducible experiments in the future and to share models and progress on the French language. For more details, please refer to the official website.

✨ Features

• FlauBERT is a pre - trained French language model, trained on a large and diverse French corpus.
• It comes with the FLUE evaluation setup, facilitating reproducible experiments in French NLP.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

import torch
from transformers import FlaubertModel, FlaubertTokenizer

# Choose among ['flaubert/flaubert_small_cased', 'flaubert/flaubert_base_uncased', 
#               'flaubert/flaubert_base_cased', 'flaubert/flaubert_large_cased']
modelname = 'flaubert/flaubert_base_cased' 

# Load pretrained model and tokenizer
flaubert, log = FlaubertModel.from_pretrained(modelname, output_loading_info=True)
flaubert_tokenizer = FlaubertTokenizer.from_pretrained(modelname, do_lowercase=False)
# do_lowercase=False if using cased models, True if using uncased ones

sentence = "Le chat mange une pomme."
token_ids = torch.tensor([flaubert_tokenizer.encode(sentence)])

last_layer = flaubert(token_ids)[0]
print(last_layer.shape)
# torch.Size([1, 8, 768])  -> (batch size x number of tokens x embedding dimension)

# The BERT [CLS] token correspond to the first hidden state of the last layer
cls_embedding = last_layer[:, 0, :]

Advanced Usage

# Notes: if your `transformers` version is <=2.10.0, `modelname` should take one
# of the following values:

['flaubert-small-cased', 'flaubert-base-uncased', 'flaubert-base-cased', 'flaubert-large-cased']

📚 Documentation

FlauBERT models

⚠️ Important Note

flaubert-small-cased is partially trained, so performance is not guaranteed. Consider using it for debugging purposes only.

📄 License

This project is licensed under the MIT license.

📚 References

If you use FlauBERT or the FLUE Benchmark for your scientific publication, or if you find the resources in this repository useful, please cite one of the following papers:

LREC paper

@InProceedings{le2020flaubert,
  author    = {Le, Hang  and  Vial, Lo\"{i}c  and  Frej, Jibril  and  Segonne, Vincent  and  Coavoux, Maximin  and  Lecouteux, Benjamin  and  Allauzen, Alexandre  and  Crabb\'{e}, Beno\^{i}t  and  Besacier, Laurent  and  Schwab, Didier},
  title     = {FlauBERT: Unsupervised Language Model Pre-training for French},
  booktitle = {Proceedings of The 12th Language Resources and Evaluation Conference},
  month     = {May},
  year      = {2020},
  address   = {Marseille, France},
  publisher = {European Language Resources Association},
  pages     = {2479--2490},
  url       = {https://www.aclweb.org/anthology/2020.lrec-1.302}
}

TALN paper

@inproceedings{le2020flaubert,
  title         = {FlauBERT: des mod{\`e}les de langue contextualis{\'e}s pr{\'e}-entra{\^\i}n{\'e}s pour le fran{\c{c}}ais},
  author        = {Le, Hang and Vial, Lo{\"\i}c and Frej, Jibril and Segonne, Vincent and Coavoux, Maximin and Lecouteux, Benjamin and Allauzen, Alexandre and Crabb{\'e}, Beno{\^\i}t and Besacier, Laurent and Schwab, Didier},
  booktitle     = {Actes de la 6e conf{\'e}rence conjointe Journ{\'e}es d'{\'E}tudes sur la Parole (JEP, 31e {\'e}dition), Traitement Automatique des Langues Naturelles (TALN, 27e {\'e}dition), Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (R{\'E}CITAL, 22e {\'e}dition). Volume 2: Traitement Automatique des Langues Naturelles},
  pages         = {268--278},
  year          = {2020},
  organization  = {ATALA}
}