Datasets:
web_questions

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and 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.

	# Lint as: python3
	"""WebQuestions Benchmark for Question Answering."""


	import json
	import re

	import datasets


	_CITATION = """
	@inproceedings{berant-etal-2013-semantic,
	title = "Semantic Parsing on {F}reebase from Question-Answer Pairs",
	author = "Berant, Jonathan and
	Chou, Andrew and
	Frostig, Roy and
	Liang, Percy",
	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/D13-1160",
	pages = "1533--1544",
	}
	"""
	_SPLIT_DOWNLOAD_URL = {
	"train": "https://worksheets.codalab.org/rest/bundles/0x4a763f8cde224c2da592b75f29e2f5c2/contents/blob/",
	"test": "https://worksheets.codalab.org/rest/bundles/0xe7bac352fce7448c9ef238fb0a297ec2/contents/blob/",
	}

	_DESCRIPTION = """\
	This dataset consists of 6,642 question/answer pairs.
	The questions are supposed to be answerable by Freebase, a large knowledge graph.
	The questions are mostly centered around a single named entity.
	The questions are popular ones asked on the web (at least in 2013).
	"""


	class WebQuestions(datasets.GeneratorBasedBuilder):
	"""WebQuestions Benchmark for Question Answering."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"url": datasets.Value("string"),
	"question": datasets.Value("string"),
	"answers": datasets.features.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=None,
	homepage="https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	file_paths = dl_manager.download(_SPLIT_DOWNLOAD_URL)

	return [
	datasets.SplitGenerator(name=split, gen_kwargs={"file_path": file_path})
	for split, file_path in file_paths.items()
	]

	def _generate_examples(self, file_path):
	"""Parses split file and yields examples."""

	def _target_to_answers(target):
	target = re.sub(r"^$list \|$$", "", target)
	return ["".join(ans) for ans in re.findall(r'$description (?:"([^"]+?)"\|([^)]+?))$\w*', target)]

	with open(file_path, encoding="utf-8") as f:
	examples = json.load(f)
	for i, ex in enumerate(examples):
	yield i, {
	"url": ex["url"],
	"question": ex["utterance"],
	"answers": _target_to_answers(ex["targetValue"]),
	}