Datasets:

ipipan
/

polqa

	# 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.


	import csv
	import json

	import datasets


	_CITATION = """\
	@misc{rybak2022improving,
	title={Improving Question Answering Performance through Manual Annotation: Costs, Benefits and Strategies},
	author={Piotr Rybak and Piotr Przybyła and Maciej Ogrodniczuk},
	year={2022},
	eprint={2212.08897},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """\
	PolQA is the first Polish dataset for OpenQA. It consists of 7,000 questions, 87,525 manually labeled evidence passages, and a corpus of over 7 million candidate passages.
	"""

	_HOMEPAGE = ""

	_LICENSE = ""

	_FEATURES_PAIRS = datasets.Features(
	{
	"question_id": datasets.Value("int32"),
	"passage_title": datasets.Value("string"),
	"passage_text": datasets.Value("string"),
	"passage_wiki": datasets.Value("string"),
	"passage_id": datasets.Value("string"),
	"duplicate": datasets.Value("bool"),
	"question": datasets.Value("string"),
	"relevant": datasets.Value("bool"),
	"annotated_by": datasets.Value("string"),
	"answers": datasets.Value("string"),
	"question_formulation": datasets.Value("string"),
	"question_type": datasets.Value("string"),
	"entity_type": datasets.Value("string"),
	"entity_subtype": datasets.Value("string"),
	"split": datasets.Value("string"),
	"passage_source": datasets.Value("string"),
	}
	)

	_FEATURES_PASSAGES = datasets.Features(
	{
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"text": datasets.Value("string"),
	}
	)

	_URLS = {
	"pairs": {
	"train": ["data/train.csv"],
	"validation": ["data/valid.csv"],
	"test": ["data/test.csv"],
	},
	"passages": {
	"train": ["data/passages.jsonl"],
	},
	}


	class PolQA(datasets.GeneratorBasedBuilder):
	"""PolQA is the first Polish dataset for OpenQA. It consists of manually labeled QA pairs and a corpus of Wikipedia passages."""

	BUILDER_CONFIGS = list(map(lambda x: datasets.BuilderConfig(name=x, version=datasets.Version("1.0.0")), _URLS.keys()))
	DEFAULT_CONFIG_NAME = "pairs"

	def _info(self):
	if self.config.name == "pairs":
	features = _FEATURES_PAIRS
	else:
	features = _FEATURES_PASSAGES

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	urls = _URLS[self.config.name]
	data_dir = dl_manager.download_and_extract(urls)
	if self.config.name == "pairs":
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepaths": data_dir["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepaths": data_dir["validation"],
	"split": "validation",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepaths": data_dir["test"],
	"split": "test",
	},
	),
	]
	else:
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepaths": data_dir["train"],
	"split": "train",
	},
	),
	]

	@staticmethod
	def _parse_bool(text):
	if text == 'True':
	return True
	elif text == 'False':
	return False
	else:
	raise ValueError

	def _generate_examples(self, filepaths, split):
	if self.config.name == "pairs":
	boolean_features = [name for name, val in _FEATURES_PAIRS.items() if val.dtype == "bool"]

	for filepath in filepaths:
	with open(filepath, encoding="utf-8") as f:
	data = csv.DictReader(f)
	for i, row in enumerate(data):
	for boolean_feature in boolean_features:
	row[boolean_feature] = self._parse_bool(row[boolean_feature])
	yield i, row
	else:
	for filepath in filepaths:
	with open(filepath, encoding="utf-8") as f:
	for i, row in enumerate(f):
	parsed_row = json.loads(row)
	yield i, parsed_row