Datasets:
mwsc

	"""A modification of the Winograd Schema Challenge to ensure answers are a single context word"""

	import os
	import re

	import datasets


	_CITATION = """\
	@article{McCann2018decaNLP,
	title={The Natural Language Decathlon: Multitask Learning as Question Answering},
	author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
	journal={arXiv preprint arXiv:1806.08730},
	year={2018}
	}
	"""

	_DESCRIPTION = """\
	Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
	This modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
	"""

	_DATA_URL = "https://raw.githubusercontent.com/salesforce/decaNLP/1e9605f246b9e05199b28bde2a2093bc49feeeaa/local_data/schema.txt"
	# Alternate: https://s3.amazonaws.com/research.metamind.io/decaNLP/data/schema.txt


	class MWSC(datasets.GeneratorBasedBuilder):
	"""MWSC: modified Winograd Schema Challenge"""

	VERSION = datasets.Version("0.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"question": datasets.Value("string"),
	"options": datasets.features.Sequence(datasets.Value("string")),
	"answer": 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://decanlp.com",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	schemas_file = dl_manager.download_and_extract(_DATA_URL)

	if os.path.isdir(schemas_file):
	# During testing the download manager mock gives us a directory
	schemas_file = os.path.join(schemas_file, "schema.txt")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": schemas_file, "split": "train"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": schemas_file, "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": schemas_file, "split": "dev"},
	),
	]

	def _get_both_schema(self, context):
	"""Split [option1/option2] into 2 sentences.
	From https://github.com/salesforce/decaNLP/blob/1e9605f246b9e05199b28bde2a2093bc49feeeaa/text/torchtext/datasets/generic.py#L815-L827"""
	pattern = r"\[.*\]"
	variations = [x[1:-1].split("/") for x in re.findall(pattern, context)]
	splits = re.split(pattern, context)
	results = []
	for which_schema in range(2):
	vs = [v[which_schema] for v in variations]
	context = ""
	for idx in range(len(splits)):
	context += splits[idx]
	if idx < len(vs):
	context += vs[idx]
	results.append(context)
	return results

	def _generate_examples(self, filepath, split):
	"""Yields examples."""

	schemas = []
	with open(filepath, encoding="utf-8") as schema_file:
	schema = []
	for line in schema_file:
	if len(line.split()) == 0:
	schemas.append(schema)
	schema = []
	continue
	else:
	schema.append(line.strip())

	# Train/test/dev split from decaNLP code
	splits = {}
	traindev = schemas[:-50]
	splits["test"] = schemas[-50:]
	splits["train"] = traindev[:40]
	splits["dev"] = traindev[40:]

	idx = 0
	for schema in splits[split]:
	sentence, question, answers = schema
	sentence = self._get_both_schema(sentence)
	question = self._get_both_schema(question)
	answers = answers.split("/")
	for i in range(2):
	yield idx, {"sentence": sentence[i], "question": question[i], "options": answers, "answer": answers[i]}
	idx += 1