Delete loading script

medmcqa.py

@@ -1,116 +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.
-"""MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering"""
-
-
-import json
-import os
-
-import datasets
-
-
-_DESCRIPTION = """\
-MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.
-MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity.
-The dataset contains questions about the following topics: Anesthesia, Anatomy, Biochemistry, Dental, ENT, Forensic Medicine (FM)
-Obstetrics and Gynecology (O&G), Medicine, Microbiology, Ophthalmology, Orthopedics Pathology, Pediatrics, Pharmacology, Physiology,
-Psychiatry, Radiology Skin, Preventive & Social Medicine (PSM) and Surgery
-"""
-
-
-_HOMEPAGE = "https://medmcqa.github.io"
-
-_LICENSE = "Apache License 2.0"
-_URL = "https://drive.google.com/uc?export=download&id=15VkJdq5eyWIkfb_aoD3oS8i4tScbHYky"
-_CITATION = """\
-@InProceedings{pmlr-v174-pal22a,
-  title = 	 {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
-  author =       {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
-  booktitle = 	 {Proceedings of the Conference on Health, Inference, and Learning},
-  pages = 	 {248--260},
-  year = 	 {2022},
-  editor = 	 {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},
-  volume = 	 {174},
-  series = 	 {Proceedings of Machine Learning Research},
-  month = 	 {07--08 Apr},
-  publisher =    {PMLR},
-  pdf = 	 {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},
-  url = 	 {https://proceedings.mlr.press/v174/pal22a.html},
-  abstract = 	 {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study.}
-}
-"""
-
-
-class MedMCQA(datasets.GeneratorBasedBuilder):
-    """MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-
-        features = datasets.Features(
-            {
-                "id": datasets.Value("string"),
-                "question": datasets.Value("string"),
-                "opa": datasets.Value("string"),
-                "opb": datasets.Value("string"),
-                "opc": datasets.Value("string"),
-                "opd": datasets.Value("string"),
-                "cop": datasets.features.ClassLabel(names=["a", "b", "c", "d"]),
-                "choice_type": datasets.Value("string"),
-                "exp": datasets.Value("string"),
-                "subject_name": datasets.Value("string"),
-                "topic_name": datasets.Value("string"),
-            }
-        )
-        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.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "test.json"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir, "dev.json"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                data["cop"] = int(data.get("cop", 0)) - 1
-                data["exp"] = data.get("exp", "")
-                yield key, data