Datasets:
mc_taco

	# coding=utf-8
	# Copyright 2020 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.
	"""MC-TACO Dataset."""


	import csv

	import datasets


	_CITATION = """\
	@inproceedings{ZKNR19,
	author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
	title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
	booktitle = {EMNLP},
	year = {2019},
	}
	"""

	_DESCRIPTION = """\
	MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer
	pairs that require temporal commonsense comprehension. A system receives a sentence
	providing context information, a question designed to require temporal commonsense
	knowledge, and multiple candidate answers. More than one candidate answer can be plausible.

	The task is framed as binary classification: givent he context, the question,
	and the candidate answer, the task is to determine whether the candidate
	answer is plausible ("yes") or not ("no")."""

	_LICENSE = "Unknown"

	_URLs = {
	"dev": "https://raw.githubusercontent.com/CogComp/MCTACO/master/dataset/dev_3783.tsv",
	"test": "https://raw.githubusercontent.com/CogComp/MCTACO/master/dataset/test_9442.tsv",
	}


	class McTaco(datasets.GeneratorBasedBuilder):
	"""MC-TACO Dataset: temporal commonsense knowledge."""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="plain_text",
	description="Plain text",
	version=VERSION,
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answer": datasets.Value("string"),
	"label": datasets.ClassLabel(names=["no", "yes"]),
	"category": datasets.ClassLabel(
	names=["Event Duration", "Event Ordering", "Frequency", "Typical Time", "Stationarity"]
	),
	}
	),
	supervised_keys=None,
	homepage="https://cogcomp.seas.upenn.edu/page/resource_view/125",
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URLs)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": data_dir["test"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": data_dir["dev"],
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(
	csv_file,
	delimiter="\t",
	)
	for id_, row in enumerate(csv_reader):
	yield id_, {
	"sentence": row[0],
	"question": row[1],
	"answer": row[2],
	"label": row[3],
	"category": row[4],
	}