Datasets:

KETI-AIR
/

korquad

Dataset card Files Files and versions Community

korquad / korquad.py

kimsan0622

initial commit

4fa57a6 almost 3 years ago

raw history blame contribute delete

No virus

10.9 kB

	# Copyright 2021 san kim
	#
	# 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.

	# coding=utf-8
	# Copyright 2021 The TensorFlow Datasets Authors.
	#
	# 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.

	# modified by kimsan0622@keti.re.kr
	"""korquad dataset."""
	import os
	import json
	import copy
	import glob
	import hashlib
	import functools

	import datasets

	# KorQuad: https://korquad.github.io/
	# ---------------------------------------------
	_KORQUAD_URL='https://korquad.github.io/'
	# https://github.com/korquad/korquad.github.io/raw/master/dataset/KorQuAD_v1.0_dev.json
	_KORQUAD_ROOT='https://github.com/korquad/korquad.github.io/raw/master/dataset/'
	_KORQUADV1_TRAIN_LINK=[os.path.join(_KORQUAD_ROOT, 'KorQuAD_v1.0_train.json')]
	_KORQUADV1_DEV_LINK=[os.path.join(_KORQUAD_ROOT, 'KorQuAD_v1.0_dev.json')]
	_KORQUADV1_DEFAULT_SPLIT={'train': _KORQUADV1_TRAIN_LINK, 'dev': _KORQUADV1_DEV_LINK}
	_KORQUADV1_DESCRIPTION = """
	KorQuAD1.0
	"""
	_KORQUADV1_CITATION = """
	@article{DBLP:journals/corr/abs-1909-07005,
	author = {Seungyoung Lim and
	Myungji Kim and
	Jooyoul Lee},
	title = {KorQuAD1.0: Korean {QA} Dataset for Machine Reading Comprehension},
	journal = {CoRR},
	volume = {abs/1909.07005},
	year = {2019},
	url = {http://arxiv.org/abs/1909.07005},
	archivePrefix = {arXiv},
	eprint = {1909.07005},
	timestamp = {Mon, 23 Sep 2019 18:07:15 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-1909-07005.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	# https://github.com/korquad/korquad.github.io/raw/master/dataset/KorQuAD_2.1/train/KorQuAD_2.1_train_00.zip
	_KORQUADV2_TRAIN_LINK=[os.path.join(_KORQUAD_ROOT,'KorQuAD_2.1/train', 'KorQuAD_2.1_train_{0:02d}.zip'.format(idx)) for idx in range(13)]
	_KORQUADV2_DEV_LINK=[os.path.join(_KORQUAD_ROOT,'KorQuAD_2.1/dev', 'KorQuAD_2.1_dev_{0:02d}.zip'.format(idx)) for idx in range(2)]
	_KORQUADV2_DEFAULT_SPLIT={'train': _KORQUADV2_TRAIN_LINK, 'dev': _KORQUADV2_DEV_LINK}
	_KORQUADV2_DESCRIPTION = """
	KorQuAD2.1
	"""
	_KORQUADV2_CITATION = """
	김영민, 임승영, 이현정, 박소윤, 김명지. (2020). KorQuAD 2.0: 웹문서 기계독해를 위한 한국어 질의응답 데이터셋. 정보과학회논문지, 47(6), 577-586.
	"""


	SQUADLIKE_FEATURES = datasets.Features({
	"id":
	datasets.Value("string"),
	"title":
	datasets.Value("string"),
	"context":
	datasets.Value("string"),
	"question":
	datasets.Value("string"),
	"answers":
	datasets.Sequence({
	"text": datasets.Value("string"),
	"answer_start": datasets.Value("int32"),
	}),
	})

	# adopted from question_answering in tensorflow_datasets
	def generate_squadlike_examples(filepath):
	"""Parses a SQuAD-like JSON, yielding examples with `SQUADLIKE_FEATURES`."""
	# We first re-group the answers, which may be flattened (e.g., by XTREME).
	qas = {}
	with open(filepath) as f:
	squad = json.load(f)
	for article in squad["data"]:
	title = article.get("title", "")
	for paragraph in article["paragraphs"]:
	context = paragraph["context"]
	for qa in paragraph["qas"]:
	qa["title"] = title
	qa["context"] = context
	id_ = qa["id"]
	if id_ in qas:
	qas[id_]["answers"].extend(qa["answers"])
	else:
	qas[id_] = qa

	for id_, qa in qas.items():
	answer_starts = [answer["answer_start"] for answer in qa["answers"]]
	answers = [answer["text"] for answer in qa["answers"]]
	yield id_, {
	"title": qa["title"],
	"context": qa["context"],
	"question": qa["question"],
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}

	_KORQUADV2_KEY_MAP={'context':'context', 'answer_start': 'answer_start', 'text':'text'}
	_KORQUADV2_HTML_KEY_MAP={'context':'raw_html', 'answer_start': 'html_answer_start', 'text':'html_answer_text'}

	def generate_korquadv2_examples(filepath, KEY_MAP):
	qas = {}
	with open(filepath) as f:
	squad = json.load(f)
	for article in squad["data"]:
	title = article.get("title", "").strip()
	context = article[KEY_MAP['context']]
	for qa in article["qas"]:
	qa["title"] = title
	qa["context"] = context
	id_ = qa["id"]
	qa["answers"] = [copy.deepcopy(qa["answer"])]
	del qa["answer"]
	qas[id_] = qa

	for id_, qa in qas.items():
	answer_starts = [answer[KEY_MAP['answer_start']] for answer in qa["answers"]]
	answers = [answer[KEY_MAP['text']] for answer in qa["answers"]]
	yield id_, {
	"title": qa["title"],
	"context": qa["context"],
	"question": qa["question"].strip(),
	"id": id_,
	"answers": {
	"answer_start": answer_starts,
	"text": answers,
	},
	}

	_KORQUAD_MANUAL_SPLIT = {
	'source': {
	datasets.Split.TRAIN: ['train'],
	datasets.Split.VALIDATION: ['train'],
	datasets.Split.TEST: ['dev'],
	},
	'split': {
	datasets.Split.TRAIN: lambda x: x % 10 != 0,
	datasets.Split.VALIDATION: lambda x: x % 10 == 0,
	datasets.Split.TEST: lambda x: True,
	}}

	def _update_split(file_dict, split_dict):
	source_dict = split_dict['source']
	return_dict = {}
	for k, v in source_dict.items():
	flist = []
	for vv in v:
	flist.extend(file_dict[vv] if isinstance(file_dict[vv], list) else [file_dict[vv]])
	return_dict[k] = flist
	return return_dict

	def _hash_text(text):
	return hashlib.md5(text.encode("utf-8")).hexdigest()

	def _filter_fn_hash_id(uid, split_fn):
	hash_id = _hash_text(str(uid))
	val = int(hash_id, 16)
	return split_fn(val)


	_VERSION = datasets.Version('1.0.0', "")

	class KorquadConfig(datasets.BuilderConfig):
	def __init__( self,
	name,
	data_url,
	description,
	citation,
	manual_split=None,
	**kwargs):
	super(KorquadConfig, self).__init__(
	name=name,
	version=_VERSION,
	**kwargs
	)
	self.data_url=data_url
	self.description=description
	self.citation=citation
	self.manual_split=manual_split

	class Korquad(datasets.GeneratorBasedBuilder):
	"""DatasetBuilder for korquad dataset."""
	RELEASE_NOTES = {
	'1.0.0': 'Initial release.',
	}

	BUILDER_CONFIGS = [
	KorquadConfig(
	'v1.0',
	data_url=_KORQUADV1_DEFAULT_SPLIT,
	description=_KORQUADV1_DESCRIPTION,
	citation=_KORQUADV1_CITATION,
	),
	KorquadConfig(
	'v1.0.split',
	data_url=_KORQUADV1_DEFAULT_SPLIT,
	description=_KORQUADV1_DESCRIPTION,
	citation=_KORQUADV1_CITATION,
	manual_split=_KORQUAD_MANUAL_SPLIT,
	),
	KorquadConfig(
	'v2.1',
	data_url=_KORQUADV2_DEFAULT_SPLIT,
	description=_KORQUADV2_DESCRIPTION,
	citation=_KORQUADV2_CITATION,
	),
	KorquadConfig(
	'v2.1.split',
	data_url=_KORQUADV2_DEFAULT_SPLIT,
	description=_KORQUADV2_DESCRIPTION,
	citation=_KORQUADV2_CITATION,
	manual_split=_KORQUAD_MANUAL_SPLIT,
	),
	KorquadConfig(
	'v2.1.html',
	data_url=_KORQUADV2_DEFAULT_SPLIT,
	description=_KORQUADV2_DESCRIPTION,
	citation=_KORQUADV2_CITATION,
	),
	KorquadConfig(
	'v2.1.html.split',
	data_url=_KORQUADV2_DEFAULT_SPLIT,
	description=_KORQUADV2_DESCRIPTION,
	citation=_KORQUADV2_CITATION,
	manual_split=_KORQUAD_MANUAL_SPLIT,
	),
	]

	def _info(self) -> datasets.DatasetInfo:
	"""Returns the dataset metadata."""
	features_dict = SQUADLIKE_FEATURES

	return datasets.DatasetInfo(
	description=self.config.description,
	features=features_dict,
	homepage=_KORQUAD_URL,
	citation=self.config.citation,
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager):
	"""Returns SplitGenerators."""

	path_kv = {k:dl_manager.download_and_extract(v) for k, v in self.config.data_url.items()}
	if not self.config.name.startswith("v1.0"):
	for k, v in path_kv.items():
	file_names = []
	for vv in v:
	file_names.extend(glob.glob(os.path.join(vv, "*.json")))
	path_kv[k] = file_names

	if self.config.manual_split is not None:
	path_kv = _update_split(path_kv, self.config.manual_split)
	split_fn = self.config.manual_split['split']
	#return {k:self._generate_examples(v, split_fn[k]) for k, v in path_kv.items()}
	return [datasets.SplitGenerator(name=k, gen_kwargs={'path_list': v, 'split_fn': split_fn[k]}) for k, v in path_kv.items()]

	# TODO(korquad): Returns the Dict[split names, Iterator[Key, Example]]
	#return {k:self._generate_examples(v) for k, v in path_kv.items()}
	return [datasets.SplitGenerator(name=k, gen_kwargs={'path_list': v}) for k, v in path_kv.items()]

	def _generate_examples(self, path_list, split_fn=None):
	"""Yields examples."""
	# TODO(korquad): Yields (key, example) tuples from the dataset
	if self.config.name.startswith("v2.1.html"):
	gen_fn = functools.partial(generate_korquadv2_examples, KEY_MAP=_KORQUADV2_HTML_KEY_MAP)
	elif self.config.name.startswith("v2.1"):
	gen_fn = functools.partial(generate_korquadv2_examples, KEY_MAP=_KORQUADV2_KEY_MAP)
	else:
	gen_fn = generate_squadlike_examples

	if split_fn is not None:
	split_filter = functools.partial(_filter_fn_hash_id, split_fn=split_fn)
	else:
	split_filter = lambda x: True

	_hash_set = set()

	for fpath in path_list:
	for example in iter(gen_fn(fpath)):
	uid, _ = example
	if split_filter(str(uid)) and str(uid) not in _hash_set:
	_hash_set.add(str(uid))
	yield example

	# tfds build --data_dir ../../tmp/tensorflow_datasets --config v1.0.split