Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
extended|trivia_qa
Tags:
License:
# 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 | |