Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
open-domain-qa
Languages:
English
Size:
10K - 100K
ArXiv:
License:
File size: 3,705 Bytes
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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.
"""TWEETQA: A Social Media Focused Question Answering Dataset"""
import json
import os
import datasets
_CITATION = """\
@inproceedings{xiong2019tweetqa,
title={TweetQA: A Social Media Focused Question Answering Dataset},
author={Xiong, Wenhan and Wu, Jiawei and Wang, Hong and Kulkarni, Vivek and Yu, Mo and Guo, Xiaoxiao and Chang, Shiyu and Wang, William Yang},
booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={2019}
}
"""
_DESCRIPTION = """\
TweetQA is the first dataset for QA on social media data by leveraging news media and crowdsourcing.
"""
_HOMEPAGE = "https://tweetqa.github.io/"
_LICENSE = "CC BY-SA 4.0"
_URL = "https://sites.cs.ucsb.edu/~xwhan/datasets/tweetqa.zip"
class TweetQA(datasets.GeneratorBasedBuilder):
"""TweetQA: first large-scale dataset for QA over social media data"""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"Question": datasets.Value("string"),
"Answer": datasets.Sequence(datasets.Value("string")),
"Tweet": datasets.Value("string"),
"qid": 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(_URL)
train_path = os.path.join(data_dir, "TweetQA_data", "train.json")
test_path = os.path.join(data_dir, "TweetQA_data", "test.json")
dev_path = os.path.join(data_dir, "TweetQA_data", "dev.json")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_path,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": test_path,
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": dev_path,
"split": "dev",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
tweet_qa = json.load(f)
idx = 0
for data in tweet_qa:
yield idx, {
"Question": data["Question"],
"Answer": [] if split == "test" else data["Answer"],
"Tweet": data["Tweet"],
"qid": data["qid"],
}
idx += 1
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