Datasets:
liveqa

Sub-tasks: extractive-qa
Languages: Chinese
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: found
Source Datasets: original
License: unknown
liveqa / liveqa.py
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Update files from the datasets library (from 1.6.1)
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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.
"""LiveQA dataset."""
import json
import datasets
_CITATION = """\
@inproceedings{qianying-etal-2020-liveqa,
title = "{L}ive{QA}: A Question Answering Dataset over Sports Live",
author = "Qianying, Liu and
Sicong, Jiang and
Yizhong, Wang and
Sujian, Li",
booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
month = oct,
year = "2020",
address = "Haikou, China",
publisher = "Chinese Information Processing Society of China",
url = "https://www.aclweb.org/anthology/2020.ccl-1.98",
pages = "1057--1067"
}
"""
_DESCRIPTION = """\
This is LiveQA, a Chinese dataset constructed from play-by-play live broadcast.
It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games,
which are collected from the Chinese Hupu website.
"""
_HOMEPAGE = "https://github.com/PKU-TANGENT/LiveQA"
_REPO = "https://raw.githubusercontent.com/PKU-TANGENT/LiveQA/master/"
_URLs = [f"{_REPO}LiveQA-{i}.json" for i in range(1, 6)]
class LiveQA(datasets.GeneratorBasedBuilder):
"""LiveQA dataset."""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("int64"),
"passages": datasets.Sequence(
{
"is_question": datasets.Value("bool"),
"text": datasets.Value("string"),
"candidate1": datasets.Value("string"),
"candidate2": datasets.Value("string"),
"answer": datasets.Value("string"),
}
),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# No default split.
# Data is separated into 5 files due to size restrictions,
# but they must be concatenated to create a well-formed json.
data_dir = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepaths": data_dir, "split": "train"},
)
]
def _generate_examples(self, filepaths, split):
"""Yields examples."""
data_raw = ""
for filepath in filepaths:
with open(filepath, "r", encoding="utf-8") as f:
data_raw += f.read()
data = json.loads(data_raw)
games = data["passages"]
game_id = -1 # "id" field is always 1 in the original dataset regardless of game
for game in games:
game_id += 1
passages = []
for passage in game["passage"]:
is_question = "question" in passage
text = passage["question"] if is_question else passage["text"]
candidate_1 = passage["candidate1"] if is_question else ""
candidate_2 = passage["candidate2"] if is_question else ""
answer = passage["answer"] if is_question else ""
passages.append(
{
"is_question": is_question,
"text": text,
"candidate1": candidate_1,
"candidate2": candidate_2,
"answer": answer,
}
)
yield game_id, {"id": game_id, "passages": passages}