Datasets:
qa_zre

	"""A dataset reducing relation extraction to simple reading comprehension questions"""

	import csv
	import os

	import datasets


	_CITATION = """\
	@inproceedings{levy-etal-2017-zero,
	title = "Zero-Shot Relation Extraction via Reading Comprehension",
	author = "Levy, Omer and
	Seo, Minjoon and
	Choi, Eunsol and
	Zettlemoyer, Luke",
	booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
	month = aug,
	year = "2017",
	address = "Vancouver, Canada",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/K17-1034",
	doi = "10.18653/v1/K17-1034",
	pages = "333--342",
	}
	"""

	_DESCRIPTION = """\
	A dataset reducing relation extraction to simple reading comprehension questions
	"""

	_DATA_URL = "http://nlp.cs.washington.edu/zeroshot/relation_splits.tar.bz2"


	class QaZre(datasets.GeneratorBasedBuilder):
	"""QA-ZRE: Reducing relation extraction to simple reading comprehension questions"""

	VERSION = datasets.Version("0.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"relation": datasets.Value("string"),
	"question": datasets.Value("string"),
	"subject": datasets.Value("string"),
	"context": datasets.Value("string"),
	"answers": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="http://nlp.cs.washington.edu/zeroshot",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_DATA_URL)
	dl_dir = os.path.join(dl_dir, "relation_splits")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepaths": [os.path.join(dl_dir, "test." + str(i)) for i in range(10)],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepaths": [os.path.join(dl_dir, "dev." + str(i)) for i in range(10)],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepaths": [os.path.join(dl_dir, "train." + str(i)) for i in range(10)],
	},
	),
	]

	def _generate_examples(self, filepaths):
	"""Yields examples."""

	for file_idx, filepath in enumerate(filepaths):
	with open(filepath, encoding="utf-8") as f:
	data = csv.reader(f, delimiter="\t")
	for idx, row in enumerate(data):
	yield f"{file_idx}_{idx}", {
	"relation": row[0],
	"question": row[1],
	"subject": row[2],
	"context": row[3],
	"answers": row[4:],
	}