# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain""" import os import json import datasets _DESCRIPTION = """\ FrenchMedMCQA """ _HOMEPAGE = "https://frenchmedmcqa.github.io" _LICENSE = "Apache License 2.0" _URL = "https://huggingface.co/datasets/DEFT-2023/DEFT2023/resolve/main/DEFT-2023-FULL.zip" _CITATION = """\ @unpublished{labrak:hal-03824241, TITLE = {{FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain}}, AUTHOR = {Labrak, Yanis and Bazoge, Adrien and Dufour, Richard and Daille, BĂ©atrice and Gourraud, Pierre-Antoine and Morin, Emmanuel and Rouvier, Mickael}, URL = {https://hal.archives-ouvertes.fr/hal-03824241}, NOTE = {working paper or preprint}, YEAR = {2022}, MONTH = Oct, PDF = {https://hal.archives-ouvertes.fr/hal-03824241/file/LOUHI_2022___QA-3.pdf}, HAL_ID = {hal-03824241}, HAL_VERSION = {v1}, } """ class DEFT2023(datasets.GeneratorBasedBuilder): """FrenchMedMCQA : A French Multi-Choice Question Answering Corpus for Medical domain""" VERSION = datasets.Version("1.0.0") def _info(self): features = datasets.Features( { "id": datasets.Value("string"), "question": datasets.Value("string"), "answer_a": datasets.Value("string"), "answer_b": datasets.Value("string"), "answer_c": datasets.Value("string"), "answer_d": datasets.Value("string"), "answer_e": datasets.Value("string"), "correct_answers": datasets.Sequence( datasets.features.ClassLabel(names=["a", "b", "c", "d", "e"]), ), "type": datasets.Value("string"), "subject_name": datasets.Value("string"), "number_correct_answers": datasets.features.ClassLabel(names=["1","2","3","4","5"]), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" data_dir = dl_manager.download_and_extract(_URL) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "train.json"), }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "filepath": os.path.join(data_dir, "dev.json"), }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "filepath": os.path.join(data_dir, "test.json"), }, ), ] def _generate_examples(self, filepath): with open(filepath, encoding="utf-8") as f: data = json.load(f) for key, d in enumerate(data): yield key, { "id": d["id"], "question": d["question"], "answer_a": d["answers"]["a"], "answer_b": d["answers"]["b"], "answer_c": d["answers"]["c"], "answer_d": d["answers"]["d"], "answer_e": d["answers"]["e"], "correct_answers": d["correct_answers"], "number_correct_answers": str(len(d["correct_answers"])), "type": d["type"], "subject_name": d["subject_name"], }