Datasets:
head_qa

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
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	# http://www.apache.org/licenses/LICENSE-2.0
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	"""HEAD-QA: A Healthcare Dataset for Complex Reasoning"""


	import json
	import os

	import datasets


	_CITATION = """\
	@inproceedings{vilares-gomez-rodriguez-2019-head,
	title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
	author = "Vilares, David and
	G{\'o}mez-Rodr{\'i}guez, Carlos",
	booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
	month = jul,
	year = "2019",
	address = "Florence, Italy",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/P19-1092",
	doi = "10.18653/v1/P19-1092",
	pages = "960--966",
	abstract = "We present HEAD-QA, a multi-choice question answering testbed to encourage research on complex reasoning. The questions come from exams to access a specialized position in the Spanish healthcare system, and are challenging even for highly specialized humans. We then consider monolingual (Spanish) and cross-lingual (to English) experiments with information retrieval and neural techniques. We show that: (i) HEAD-QA challenges current methods, and (ii) the results lag well behind human performance, demonstrating its usefulness as a benchmark for future work.",
	}
	"""

	_DESCRIPTION = """\
	HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
	Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio
	de Sanidad, Consumo y Bienestar Social.

	The dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.
	"""

	_HOMEPAGE = "https://aghie.github.io/head-qa/"

	_LICENSE = "MIT License"

	_REPO = "https://huggingface.co/datasets/head_qa/resolve/main/data"
	_URL = f"{_REPO}/head-qa-es-en-pdfs.zip"

	_DIRS = {"es": "HEAD", "en": "HEAD_EN"}


	class HeadQA(datasets.GeneratorBasedBuilder):
	"""HEAD-QA: A Healthcare Dataset for Complex Reasoning"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="es", version=VERSION, description="Spanish HEAD dataset"),
	datasets.BuilderConfig(name="en", version=VERSION, description="English HEAD dataset"),
	]

	DEFAULT_CONFIG_NAME = "es"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"name": datasets.Value("string"),
	"year": datasets.Value("string"),
	"category": datasets.Value("string"),
	"qid": datasets.Value("int32"),
	"qtext": datasets.Value("string"),
	"ra": datasets.Value("int32"),
	"image": datasets.Image(),
	"answers": [
	{
	"aid": datasets.Value("int32"),
	"atext": datasets.Value("string"),
	}
	],
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)

	dir = _DIRS[self.config.name]
	data_lang_dir = os.path.join(data_dir, dir)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"train_{dir}.json")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"test_{dir}.json")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_lang_dir, f"dev_{dir}.json")},
	),
	]

	def _generate_examples(self, data_dir, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	head_qa = json.load(f)
	for exam_id, exam in enumerate(head_qa["exams"]):
	content = head_qa["exams"][exam]
	name = content["name"].strip()
	year = content["year"].strip()
	category = content["category"].strip()
	for question in content["data"]:
	qid = int(question["qid"].strip())
	qtext = question["qtext"].strip()
	ra = int(question["ra"].strip())
	image_path = question["image"].strip()

	aids = [answer["aid"] for answer in question["answers"]]
	atexts = [answer["atext"].strip() for answer in question["answers"]]
	answers = [{"aid": aid, "atext": atext} for aid, atext in zip(aids, atexts)]

	id_ = f"{exam_id}_{qid}"
	yield id_, {
	"name": name,
	"year": year,
	"category": category,
	"qid": qid,
	"qtext": qtext,
	"ra": ra,
	"image": os.path.join(data_dir, image_path) if image_path else None,
	"answers": answers,
	}