Datasets:
google_wellformed_query

	# 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.
	"""Google Wellformed Query Dataset"""


	import datasets


	_CITATION = """\
	@misc{faruqui2018identifying,
	title={Identifying Well-formed Natural Language Questions},
	author={Manaal Faruqui and Dipanjan Das},
	year={2018},
	eprint={1808.09419},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.
	"""

	_URL = "https://raw.githubusercontent.com/google-research-datasets/query-wellformedness/master/{}.tsv"


	class GoogleWellformedQuery(datasets.GeneratorBasedBuilder):
	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features({"rating": datasets.Value("float"), "content": datasets.Value("string")}),
	supervised_keys=None,
	homepage="https://github.com/google-research-datasets/query-wellformedness",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	tr_file = dl_manager.download_and_extract(_URL.format("train"))
	tst_file = dl_manager.download_and_extract(_URL.format("test"))
	dev_file = dl_manager.download_and_extract(_URL.format("dev"))

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": tr_file,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": tst_file,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": dev_file,
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, "r", encoding="utf-8") as file:
	reader = file.read().split("\n")
	for idx, row in enumerate(reader):
	row = row.split("\t")
	if len(row) == 1:
	continue
	yield idx, {"rating": row[1], "content": row[0]}