Delete loading script

FrenchMedMCQA.py

@@ -1,138 +0,0 @@
-# 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 = """\
-This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice \
-Question Answering (MCQA) dataset in French for medical domain. It is composed of  \
-3,105 questions taken from real exams of the French medical specialization diploma \
-in pharmacy, mixing single and multiple answers. Each instance of the dataset contains \
-an identifier, a question, five possible answers and their manual correction(s).  \
-We also propose first baseline models to automatically process this MCQA task in  \
-order to report on the current performances and to highlight the difficulty of the  \
-task. A detailed analysis of the results showed that it is necessary to have  \
-representations adapted to the medical domain or to the MCQA task: in our case,  \
-English specialized models yielded better results than generic French ones, even though  \
-FrenchMedMCQA is in French. Corpus, models and tools are available online.
-"""
-
-_HOMEPAGE = "https://frenchmedmcqa.github.io"
-
-_LICENSE = "Apache License 2.0"
-
-_URL = "https://huggingface.co/datasets/Dr-BERT/FrenchMedMCQA/resolve/main/data.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-@unpublished{labrak:hal-03824241,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, Béatrice 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 FrenchMedMCQA(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).rstrip("/")
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": data_dir + "/train.json",
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": data_dir + "/dev.json",
-                    "split": "validation",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": data_dir + "/test.json",
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-
-        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"] if split != "test" else "unknown",
-                    "subject_name": d["subject_name"],
-                }