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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/fmikaelian/camembert-base-fquad
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fmikaelian/camembert-base-fquad fmikaelian/camembert-base-fquad
500 downloads
last 30 days

pytorch

tf

Contributed by

fmikaelian Félix MIKAELIAN
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("fmikaelian/camembert-base-fquad") model = AutoModelForQuestionAnswering.from_pretrained("fmikaelian/camembert-base-fquad")

camembert-base-fquad

Description

A baseline model for question-answering in french (CamemBERT model fine-tuned on FQuAD)

Training hyperparameters

python3 ./examples/question-answering/run_squad.py \
--model_type camembert \
--model_name_or_path camembert-base \
--do_train \
--do_eval \
--do_lower_case \
--train_file train.json \
--predict_file valid.json \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir output \
--per_gpu_eval_batch_size=3 \
--per_gpu_train_batch_size=3 \
--save_steps 10000

Evaluation results

{"f1": 77.24515316052342, "exact_match": 52.82308657465496}

Usage

from transformers import pipeline

nlp = pipeline('question-answering', model='fmikaelian/camembert-base-fquad', tokenizer='fmikaelian/camembert-base-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."
})