Datasets:

sagnikrayc
/

mctest

	# 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.
	"""MCTest: Machine comprehension test: http://research.microsoft.com/mct"""


	import os
	import datasets


	_CITATION = """\
	@inproceedings{richardson-etal-2013-mctest,
	title = "{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text",
	author = "Richardson, Matthew and
	Burges, Christopher J.C. and
	Renshaw, Erin",
	booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
	month = oct,
	year = "2013",
	address = "Seattle, Washington, USA",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/D13-1020",
	pages = "193--203",
	}
	"""

	_DESCRIPTION = """\
	MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension.
	"""

	_HOMEPAGE = "https://www.aclweb.org/anthology/D13-1020/"

	_DATA_URL = "http://parl.ai/downloads/mctest/mctest.tar.gz"


	class MCTest(datasets.GeneratorBasedBuilder):
	"""MCTest: Machine comprehension test: http://research.microsoft.com/mct"""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="mc500",
	version=VERSION,
	description="MC 500",
	),
	datasets.BuilderConfig(
	name="mc160",
	version=VERSION,
	description="MC 160",
	),
	]

	DEFAULT_CONFIG_NAME = "mc500"

	def _info(self):
	if self.config.name == "mc500":
	features = datasets.Features(
	{
	"idx": dict(
	{"story": datasets.Value("string"),
	"question": datasets.Value("int32")
	}
	),
	"question": datasets.Value("string"),
	"story": datasets.Value("string"),
	"properties": dict(
	{
	"author": datasets.Value("string"),
	"work_time": datasets.Value("int32"),
	"quality_score": datasets.Value("int32"),
	"creativity_words": datasets.Sequence(datasets.Value("string")),
	}
	),
	"answer_options": dict(
	{
	"A": datasets.Value("string"),
	"B": datasets.Value("string"),
	"C": datasets.Value("string"),
	"D": datasets.Value("string")
	}
	),
	"answer": datasets.Value("string"),
	"question_is_multiple": datasets.Value("bool")
	}
	)
	else:
	features = datasets.Features(
	{
	"idx": dict(
	{"story": datasets.Value("string"),
	"question": datasets.Value("int32")
	}
	),
	"question": datasets.Value("string"),
	"story": datasets.Value("string"),
	"properties": dict(
	{
	"author": datasets.Value("string"),
	"work_time": datasets.Value("int32"),
	}
	),
	"answer_options": dict(
	{
	"A": datasets.Value("string"),
	"B": datasets.Value("string"),
	"C": datasets.Value("string"),
	"D": datasets.Value("string")
	}
	),
	"answer": datasets.Value("string"),
	"question_is_multiple": datasets.Value("bool")
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = os.path.join(dl_manager.download_and_extract(_DATA_URL), 'mctest')
	paths = {}
	for phase in ["train", "dev", "test"]:
	paths[phase] = {
	"data": os.path.join(data_dir, "MCTest", f"{self.config.name}.{phase}.tsv"),
	"answer": os.path.join(data_dir, "MCTest", f"{self.config.name}.{phase}.ans")
	}
	paths["test"]["answer"] = os.path.join(data_dir, "MCTestAnswers", f"{self.config.name}.test.ans")

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

	def _get_properties(self, property_str):
	"""
	properties is a semicolon-delimited list of property:value pairs, including
	Author (anonymized author id, consistent across all files)
	Work Time(s): Seconds between author accepting and submitting the task
	Qual. score: The author's grammar qualification test score (% correct)
	Creativity Words: Words the author was given to encourage creativity
	(there are no creativity words or qual score for mc160, see paper)
	:param property_str:
	:return:
	"""
	properties = property_str.split(';')
	property_data = {
	"author": properties[0].split(':')[-1].strip(),
	"work_time": int(properties[1].split(':')[-1].strip())
	}
	if self.config.name == "mc500":
	property_data.update(
	{
	"quality_score": int(properties[2].split(':')[-1].strip()),
	"creativity_words": properties[3].split(':')[-1].strip().split(',')
	}
	)
	return property_data

	def _generate_examples(self, filepath):
	tab_char = '\t'
	data_path = filepath['data']
	ans_path = filepath['answer']
	data_lines = open(data_path, encoding="utf-8").read().split('\n')[:-1]
	answer_lines = open(ans_path, encoding="utf-8").read().split('\n')[:-1]
	for data_line, answer_line in zip(data_lines, answer_lines):
	data_line_split = data_line.split(tab_char)
	story_id = data_line_split[0]
	properties = self._get_properties(data_line_split[1])
	story = data_line_split[2]
	answers = answer_line.split('\t')
	data_line_split = data_line_split[3:]
	for i in range(4):
	answer = answers[i]
	index = i*5
	multiple, question_text = [x.strip() for x in data_line_split[index].strip().split(':')]
	question_is_multiple = True if multiple == "multiple" else False
	answer_options = {x: y for x, y in zip(["A", "B", "C", "D"], data_line_split[index+1:index+5])}
	yield f"{story_id}-{i}", {
	"idx":
	{"story": story_id,
	"question": i
	},
	"question": question_text,
	"story": story,
	"properties": properties,
	"answer_options": answer_options,
	"answer": answer,
	"question_is_multiple": question_is_multiple
	}