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# coding=utf-8
# Copyright 2022 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.
import json
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from .bigbiohub import BigBioConfig, Tasks, qa_features
_LANGUAGES = ["English", "Spanish"]
_LICENSE = "MIT"
_LOCAL = False
_PUBMED = False
_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"
}
"""
_DATASETNAME = "head_qa"
_DISPLAYNAME = "HEAD-QA"
_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 following topics: medicine, \
nursing, psychology, chemistry, pharmacology and biology.
"""
_HOMEPAGE = "https://aghie.github.io/head-qa/"
_URLS = {
"HEAD": "https://drive.usercontent.google.com/u/0/uc?id=1dUIqVwvoZAtbX_-z5axCoe97XNcFo1No&export=download",
"HEAD_EN": "https://drive.usercontent.google.com/u/0/uc?id=1phryJg4FjCFkn0mSCqIOP2-FscAeKGV0&export=download",
}
_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"
class HeadQADataset(datasets.GeneratorBasedBuilder):
"""HEAD-QA: A Healthcare Dataset for Complex Reasoning"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
BUILDER_CONFIGS = [
BigBioConfig(
name="head_qa_en_source",
version=SOURCE_VERSION,
description="HeadQA English source schema",
schema="source",
subset_id="head_qa_en",
),
BigBioConfig(
name="head_qa_es_source",
version=SOURCE_VERSION,
description="HeadQA Spanish source schema",
schema="source",
subset_id="head_qa_es",
),
BigBioConfig(
name="head_qa_en_bigbio_qa",
version=BIGBIO_VERSION,
description="HeadQA English Question Answering BigBio schema",
schema="bigbio_qa",
subset_id="head_qa_en",
),
BigBioConfig(
name="head_qa_es_bigbio_qa",
version=BIGBIO_VERSION,
description="HeadQA Spanish Question Answering BigBio schema",
schema="bigbio_qa",
subset_id="head_qa_es",
),
]
DEFAULT_CONFIG_NAME = "head_qa_en_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
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"),
"answers": [
{
"aid": datasets.Value("int32"),
"atext": datasets.Value("string"),
}
],
}
)
elif self.config.schema == "bigbio_qa":
features = qa_features
else:
raise NotImplementedError(f"Schema {self.config.schema} is not supported")
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
if self.config.subset_id == "head_qa_en":
data_dir = Path(dl_manager.download_and_extract(_URLS["HEAD_EN"])) / "HEAD_EN"
subset_name = "HEAD_EN"
elif self.config.subset_id == "head_qa_es":
data_dir = Path(dl_manager.download_and_extract(_URLS["HEAD"])) / "HEAD"
subset_name = "HEAD"
else:
raise NotImplementedError(f"Subset {self.config.subset_id} is not supported")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"input_json_file": data_dir / f"train_{subset_name}.json",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"input_json_file": data_dir / f"dev_{subset_name}.json",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"input_json_file": data_dir / f"test_{subset_name}.json",
},
),
]
def _generate_examples(self, input_json_file: Path) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
if self.config.schema == "source":
for key, example in self._generate_source_documents(input_json_file):
yield key, example
elif self.config.schema == "bigbio_qa":
for key, example in self._generate_source_documents(input_json_file):
yield self._source_to_qa(example)
def _generate_source_documents(self, input_json_file: Path) -> Tuple[str, Dict]:
"""Generates source instances."""
with input_json_file.open("r", encoding="utf8") as file_stream:
head_qa = json.load(file_stream)
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())
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)]
instance_id = f"{exam_id}_{qid}"
instance = {
"name": name,
"year": year,
"category": category,
"qid": qid,
"qtext": qtext,
"ra": ra,
"answers": answers,
}
yield instance_id, instance
def _source_to_qa(self, example: Dict) -> Tuple[str, Dict]:
"""Converts a source example to BigBio example."""
instance = {
"id": example["name"] + "_qid_" + str(example["qid"]),
"question_id": example["qid"],
"document_id": None,
"question": example["qtext"],
"type": "multiple_choice",
"choices": [answer["atext"] for answer in example["answers"]],
"context": None,
"answer": [next(filter(lambda answer: answer["aid"] == example["ra"], example["answers"]))["atext"]],
}
return instance["id"], instance
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