Camembert Base Squadfr Fquad Piaf

🚀 camembert-base-squadFR-fquad-piaf

This is a French question-answering model. It uses the base CamemBERT fine-tuned on a combination of three French Q&A datasets, aiming to provide accurate answers to French questions.

🚀 Quick Start

This is a French question-answering model, which uses the base CamemBERT fine-tuned on a combination of three French Q&A datasets:

1. PIAFv1.1
2. FQuADv1.0
3. SQuAD-FR (SQuAD automatically translated to French)

✨ Features

• Multidataset fine - tuning: Fine - tuned on a combination of three French Q&A datasets, enhancing the model's adaptability and accuracy.
• Based on CamemBERT: Utilizes the powerful base model CamemBERT, leveraging its pre - trained language knowledge.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import pipeline

nlp = pipeline('question-answering', model='etalab-ia/camembert-base-squadFR-fquad-piaf', tokenizer='etalab-ia/camembert-base-squadFR-fquad-piaf')

nlp({
    'question': "Qui est Claude Monet?",
    'context': "Claude Monet, né le 14 novembre 1840 à Paris et mort le 5 décembre 1926 à Giverny, est un peintre français et l’un des fondateurs de l'impressionnisme."
})

🔧 Technical Details

Training hyperparameters

python run_squad.py \
--model_type camembert \
--model_name_or_path camembert-base \
--do_train --do_eval \
--train_file data/SQuAD+fquad+piaf.json \
--predict_file data/fquad_valid.json \
--per_gpu_train_batch_size 12 \ 
--learning_rate 3e-5 \ 
--num_train_epochs 4 \  
--max_seq_length 384 \ 
--doc_stride 128 \
--save_steps 10000 

Evaluation results

FQuAD v1.0 Evaluation

{"f1": 79.81, "exact_match": 55.14}

SQuAD-FR Evaluation

{"f1": 80.61, "exact_match": 59.54}

📚 Documentation

The model is a French question - answering model, using the base CamemBERT fine - tuned on three French Q&A datasets. It can be used to answer French questions based on given contexts.

📄 License

No license information is provided in the original document.

🙏 Acknowledgments

This work was performed using HPC resources from GENCI–IDRIS (Grant 2020-AD011011224).

📖 Citations

PIAF

@inproceedings{KeraronLBAMSSS20,
  author    = {Rachel Keraron and
               Guillaume Lancrenon and
               Mathilde Bras and
               Fr{\'{e}}d{\'{e}}ric Allary and
               Gilles Moyse and
               Thomas Scialom and
               Edmundo{-}Pavel Soriano{-}Morales and
               Jacopo Staiano},
  title     = {Project {PIAF:} Building a Native French Question-Answering Dataset},
  booktitle = {{LREC}},
  pages     = {5481--5490},
  publisher = {European Language Resources Association},
  year      = {2020}
}

FQuAD

@article{dHoffschmidt2020FQuADFQ,
  title={FQuAD: French Question Answering Dataset},
  author={Martin d'Hoffschmidt and Maxime Vidal and Wacim Belblidia and Tom Brendl'e and Quentin Heinrich},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.06071}
}

SQuAD-FR

 @MISC{kabbadj2018,
   author =       "Kabbadj, Ali",
   title =        "Something new in French Text Mining and Information Extraction (Universal Chatbot): Largest Q&A French training dataset (110 000+) ",
   editor =       "linkedin.com",
   month =        "November",
   year =         "2018",
   url =          "\url{https://www.linkedin.com/pulse/something-new-french-text-mining-information-chatbot-largest-kabbadj/}",
   note =         "[Online; posted 11-November-2018]",
 }

CamemBERT

HF model card : https://huggingface.co/camembert-base

@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}
}