Datasets:
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# 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
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