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# 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
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