Qamembert

🚀 QAmembert

QAmemBERT is a CamemBERT base model fine - tuned for the Question - Answering task in French. It is trained on four French Q&A datasets. These datasets include both contexts and questions with answers within the context (SQuAD 1.0 format) and those with answers outside the context (SQuAD 2.0 format). All these datasets are concatenated into a single dataset named frenchQA. A total of over 221,348 context/question/answer triplets are used to finetune this model, and 6,376 are used for testing. Our methodology is detailed in a blog post available in English or French.

🚀 Quick Start

To start using QAmemBERT, you can follow the usage examples below. It's easy to integrate into your question - answering tasks.

✨ Features

• Multiformat Datasets: Fine - tuned on both SQuAD 1.0 and SQuAD 2.0 formatted French Q&A datasets.
• Large - scale Training: Utilizes over 221,348 context/question/answer triplets for finetuning.
• Cross - context Answering: Capable of handling questions with answers both inside and outside the given context.

📦 Datasets

All these datasets were combined into a single dataset called frenchQA.

📊 Evaluation results

The evaluation was conducted using the evaluate Python package.

FQuaD 1.0 (validation)

The metric used is SQuAD 1.0.

qwant/squad_fr (validation)

The metric used is SQuAD 1.0.

frenchQA

This dataset includes questions with no answers in the context. The metric used is SQuAD 2.0.

💻 Usage Examples

Basic Usage

Example with answer in the context

from transformers import pipeline

qa = pipeline('question-answering', model='CATIE-AQ/QAmembert', tokenizer='CATIE-AQ/QAmembert')

result = qa({
    'question': "Combien de personnes utilisent le français tous les jours ?",
    'context': "Le français est une langue indo-européenne de la famille des langues romanes dont les locuteurs sont appelés francophones. Elle est parfois surnommée la langue de Molière.  Le français est parlé, en 2023, sur tous les continents par environ 321 millions de personnes : 235 millions l'emploient quotidiennement et 90 millions en sont des locuteurs natifs. En 2018, 80 millions d'élèves et étudiants s'instruisent en français dans le monde. Selon l'Organisation internationale de la francophonie (OIF), il pourrait y avoir 700 millions de francophones sur Terre en 2050."
})

if result['score'] < 0.01:
    print("La réponse n'est pas dans le contexte fourni.")
else :
    print(result['answer'])

235 millions

# details
result
{'score': 0.9945194721221924,
 'start': 269,
 'end': 281,
 'answer': '235 millions'}

Example with answer not in the context

from transformers import pipeline

qa = pipeline('question-answering', model='CATIE-AQ/QAmembert', tokenizer='CATIE-AQ/QAmembert')

result = qa({
    'question': "Quel est le meilleur vin du monde ?",
    'context': "La tour Eiffel est une tour de fer puddlé de 330 m de hauteur (avec antennes) située à Paris, à l’extrémité nord-ouest du parc du Champ-de-Mars en bordure de la Seine dans le 7e arrondissement. Son adresse officielle est 5, avenue Anatole-France.  
Construite en deux ans par Gustave Eiffel et ses collaborateurs pour l'Exposition universelle de Paris de 1889, célébrant le centenaire de la Révolution française, et initialement nommée « tour de 300 mètres », elle est devenue le symbole de la capitale française et un site touristique de premier plan : il s’agit du quatrième site culturel français payant le plus visité en 2016, avec 5,9 millions de visiteurs. Depuis son ouverture au public, elle a accueilli plus de 300 millions de visiteurs."
})

if result['score'] < 0.01:
    print("La réponse n'est pas dans le contexte fourni.")
else :
    print(result['answer'])

La réponse n'est pas dans le contexte fourni.

# details
result
{'score': 3.619904940035945e-13,
 'start': 734,
 'end': 744,
 'answer': 'visiteurs.'}

Advanced Usage

Try it through Space

A Space has been created to test the model. It is available here.

🔧 Environmental Impact

Carbon emissions were estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.

• Hardware Type: A100 PCIe 40/80GB
• Hours used: 5h and 36 min
• Cloud Provider: Private Infrastructure
• Carbon Efficiency (kg/kWh): 0.076kg (estimated from electricitymaps ; we take the average carbon intensity in France for the month of March 2023, as we are unable to use the data for the day of training, which are not available.)
• Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 0.1 kg eq. CO2

📚 Citations

QAmemBERT

@misc {qamembert2023,  
    author       = { {ALBAR, Boris and BEDU, Pierre and BOURDOIS, Loïck} },  
    organization  = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
    title        = { QAmembert (Revision 9685bc3) },  
    year         = 2023,  
    url          = { https://huggingface.co/CATIE-AQ/QAmembert},  
    doi          = { 10.57967/hf/0821 },  
    publisher    = { Hugging Face }  
}

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}
}

lincoln/newsquadfr

Hugging Face repository: https://hf.co/datasets/lincoln/newsquadfr

pragnakalp/squad_v2_french_translated

Hugging Face repository: https://hf.co/datasets/pragnakalp/squad_v2_french_translated

CamemBERT

@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}
}

📄 License

This project is licensed under the MIT license.