Datasets:
med_hop

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors.
	#
	# 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.
	"""MedHop: Reading Comprehension with Multiple Hops"""


	import json
	import os

	import datasets


	_CITATION = """\
	@misc{welbl2018constructing,
	title={Constructing Datasets for Multi-hop Reading Comprehension Across Documents},
	author={Johannes Welbl and Pontus Stenetorp and Sebastian Riedel},
	year={2018},
	eprint={1710.06481},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	MedHop is based on research paper abstracts from PubMed, and the queries are about interactions between pairs of drugs. \
	The correct answer has to be inferred by combining information from a chain of reactions of drugs and proteins.
	"""

	_URL = "https://drive.google.com/uc?export=download&id=1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA"


	class MedHopConfig(datasets.BuilderConfig):
	"""BuilderConfig for MedHop."""

	def __init__(self, masked=False, **kwargs):
	"""BuilderConfig for MedHop.

	Args:
	masked: `bool`, original or maksed data.
	**kwargs: keyword arguments forwarded to super.
	"""
	super(MedHopConfig, self).__init__(**kwargs)
	self.masked = masked


	class MedHop(datasets.GeneratorBasedBuilder):
	"""MedHop: Reading Comprehension with Multiple Hops"""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	MedHopConfig(
	name="original",
	version=datasets.Version("1.0.0"),
	description="The un-maksed MedHop dataset",
	masked=False,
	),
	MedHopConfig(
	name="masked", version=datasets.Version("1.0.0"), description="Masked MedHop dataset", masked=True
	),
	]
	BUILDER_CONFIG_CLASS = MedHopConfig
	DEFAULT_CONFIG_NAME = "original"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answer": datasets.Value("string"),
	"candidates": datasets.Sequence(datasets.Value("string")),
	"supports": datasets.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=None,
	homepage="http://qangaroo.cs.ucl.ac.uk/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	extracted_path = dl_manager.download_and_extract(_URL)

	medhop_path = os.path.join(extracted_path, "qangaroo_v1.1", "medhop")
	train_file = "train.json" if self.config.name == "original" else "train.masked.json"
	dev_file = "dev.json" if self.config.name == "original" else "dev.masked.json"

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": os.path.join(medhop_path, train_file)},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": os.path.join(medhop_path, dev_file)},
	),
	]

	def _generate_examples(self, filepath):
	with open(filepath, encoding="utf-8") as f:
	examples = json.load(f)
	for i, example in enumerate(examples):
	example["question"] = example.pop("query")
	yield example["id"], example