Datasets:

KelvinJiang
/

freebase_qa

	# 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.
	"""FreebaseQA: A Trivia-type QA Data Set over the Freebase Knowledge Graph"""


	import json

	import datasets


	_CITATION = """\
	@article{jiang2019freebaseqa,
	title={FreebaseQA: A New Factoid QA Dataset Matching Trivia-Style Question-Answer Pairs with Freebase},
	author={Jiang, Kelvin and Wu, Dekun and Jiang, Hui},
	journal={north american chapter of the association for computational linguistics},
	year={2019}
	}
	"""

	_DESCRIPTION = """\
	FreebaseQA is for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase The data set is generated by matching trivia-type question-answer pairs with subject-predicateobject triples in Freebase.
	"""

	_HOMEPAGE = "https://github.com/kelvin-jiang/FreebaseQA"

	_LICENSE = ""


	_REPO = "https://raw.githubusercontent.com/kelvin-jiang/FreebaseQA/master/"

	_URLs = {
	"train": _REPO + "FreebaseQA-train.json",
	"eval": _REPO + "FreebaseQA-eval.json",
	"dev": _REPO + "FreebaseQA-dev.json",
	}


	class FreebaseQA(datasets.GeneratorBasedBuilder):
	"""FreebaseQA: A Trivia-type QA Data Set over the Freebase Knowledge Graph"""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	features = datasets.Features(
	{
	"Question-ID": datasets.Value("string"),
	"RawQuestion": datasets.Value("string"),
	"ProcessedQuestion": datasets.Value("string"),
	"Parses": datasets.Sequence(
	{
	"Parse-Id": datasets.Value("string"),
	"PotentialTopicEntityMention": datasets.Value("string"),
	"TopicEntityName": datasets.Value("string"),
	"TopicEntityMid": datasets.Value("string"),
	"InferentialChain": datasets.Value("string"),
	"Answers": datasets.Sequence(
	{
	"AnswersMid": datasets.Value("string"),
	"AnswersName": datasets.Sequence(datasets.Value("string")),
	}
	),
	}
	),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	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.TRAIN,
	gen_kwargs={"filepath": data_dir["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": data_dir["eval"]},
	),
	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 f:
	dataset = json.load(f)

	if "Questions" in dataset:
	for data in dataset["Questions"]:
	id_ = data["Question-ID"]
	parses = []

	for item in data["Parses"]:
	answers = [answer for answer in item["Answers"]]

	parses.append(
	{
	"Parse-Id": item["Parse-Id"],
	"PotentialTopicEntityMention": item["PotentialTopicEntityMention"],
	"TopicEntityName": item["TopicEntityName"],
	"TopicEntityMid": item["TopicEntityMid"],
	"InferentialChain": item["InferentialChain"],
	"Answers": answers,
	},
	)

	question = {
	"Question-ID": data["Question-ID"],
	"RawQuestion": data["RawQuestion"],
	"ProcessedQuestion": data["ProcessedQuestion"],
	"Parses": parses,
	}

	yield id_, question