Datasets:
liveqa

	# 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}