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question-answering mask_token: <mask>
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								$
								curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"question": "Where does she live?", "context": "She lives in Berlin."}' \
https://api-inference.huggingface.co/models/illuin/camembert-large-fquad
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illuin/camembert-large-fquad illuin/camembert-large-fquad
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pytorch

tf

Contributed by

Illuin Technology company
3 team members · 3 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("illuin/camembert-large-fquad") model = AutoModelForQuestionAnswering.from_pretrained("illuin/camembert-large-fquad")

camembert-large-fquad

Description

A native French Question Answering model CamemBERT-large fine-tuned on FQuAD.

FQuAD Leaderboard and evaluation scores

The results of Camembert-large-fquad can be compared with other state-of-the-art models of the FQuAD Leaderboard.

On the test set the model scores,

{"f1": 91.5, "exact_match": 82.0}

On the development set the model scores,

{"f1": 91.0, "exact_match": 81.2}

Note : You can also explore the results of the model on FQuAD-Explorer !

Usage

from transformers import pipeline

nlp = pipeline('question-answering', model='illuin/camembert-large-fquad', tokenizer='illuin/camembert-large-fquad')

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."
})

Citation

If you use our work, please cite:

@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}
}