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QAmembert2

Question Answering
Transformers
Safetensors
French
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metadata
language: fr
datasets:
  - etalab-ia/piaf
  - fquad
  - lincoln/newsquadfr
  - pragnakalp/squad_v2_french_translated
  - CATIE-AQ/frenchQA
library_name: transformers
license: mit
base_model: almanach/camembertv2-base
metrics:
  - f1
  - exact_match
widget:
  - text: 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.
co2_eq_emissions: 66

QAmemBERT2

Model Description

We present QAmemBERT2, which is a CamemBERT v2 base fine-tuned for the Question-Answering task for the French language on four French Q&A datasets composed of contexts and questions with their answers inside the context (= SQuAD 1.0 format) but also contexts and questions with their answers not inside the context (= SQuAD 2.0 format). All these datasets were concatenated into a single dataset that we called frenchQA. This represents a total of over 221,348 context/question/answer triplets used to finetune this model and 6,376 to test it.
Our methodology is described in a blog post available in English or French.

Results (french QA test split)

Model Parameters Context Exact_match F1 Answer_F1 NoAnswer_F1
etalab/camembert-base-squadFR-fquad-piaf 110M 512 tokens 39.30 51.55 79.54 23.58
QAmembert 110M 512 tokens 77.14 86.88 75.66 98.11
QAmembert2 (this version) 112M 1024 tokens 76.47 88.25 78.66 97.84
QAmembert-large 336M 512 tokens 77.14 88.74 78.83 98.65
QAmemberta 111M 1024 tokens 78.18 89.53 81.40 97.64

Looking at the “Answer_f1” column, Etalab's model appears to be competitive on texts where the answer to the question is indeed in the text provided (it does better than QAmemBERT-large, for example). However, the fact that it doesn't handle texts where the answer to the question is not in the text provided is a drawback.
In all cases, whether in terms of metrics, number of parameters or context size, QAmemBERTa achieves the best results.
We therefore invite the reader to choose this model.

Usage 

from transformers import pipeline

qa = pipeline('question-answering', model='CATIE-AQ/QAmembert2', tokenizer='CATIE-AQ/QAmembert2')

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'])

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: 4h and 47 min
  • Cloud Provider: Private Infrastructure
  • Carbon Efficiency (kg/kWh): 0.055kg (estimated from electricitymaps ; we take the carbon intensity in France for November 21, 2024.)
  • Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 0.066 kg eq. CO2

Citations

QAmemBERT2 & QAmemBERTa 

@misc {qamemberta2024,
    author       = { {BOURDOIS, Loïck} },  
    organization  = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
    title        = { QAmemberta (Revision 976a70b) },
    year         = 2024,
    url          = { https://huggingface.co/CATIE-AQ/QAmemberta },
    doi          = { 10.57967/hf/3639 },
    publisher    = { Hugging Face }
}

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 }  
}

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}
}

CamemBERT 2.0 

@misc{antoun2024camembert20smarterfrench,
      title={CamemBERT 2.0: A Smarter French Language Model Aged to Perfection}, 
      author={Wissam Antoun and Francis Kulumba and Rian Touchent and Éric de la Clergerie and Benoît Sagot and Djamé Seddah},
      year={2024},
      eprint={2411.08868},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2411.08868}, 
}

frenchQA 

@misc {frenchQA2023,  
    author       = { {ALBAR, Boris and BEDU, Pierre and BOURDOIS, Loïck} },  
    organization  = { {Centre Aquitain des Technologies de l'Information et Electroniques} },  
    title        = { frenchQA (Revision 6249cd5) },  
    year         = 2023,  
    url          = { https://huggingface.co/CATIE-AQ/frenchQA },  
    doi          = { 10.57967/hf/0862 },  
    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

License

MIT