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DEFT2023.py

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+# 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"
+
+_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+@unpublished{labrak:hal-03824241,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 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."""
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join("../json/", "train.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join("../json/", "dev.json"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join("../json/", "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"],
+                }

test_dataset.py

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+from datasets import load_dataset
+
+dataset = load_dataset("./DEFT2023.py")
+
+print("#"*50)
+print(dataset)
+print("#"*50)
+print(dataset["train"])
+print("#"*50)
+print(dataset["train"][0])
+print("#"*50)
+
+print(dataset["train"].features["number_correct_answers"].names)
+
+label_list = dataset["train"].features["correct_answers"].feature.names
+print(label_list)