Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
open-domain-qa
Languages:
Polish
Size:
1K - 10K
License:
# 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. | |
"""Did You Know? dataset""" | |
from __future__ import absolute_import, division, print_function | |
import csv | |
import os | |
import datasets | |
_CITATION = """\ | |
@inproceedings{marcinczuk2013open, | |
title={Open dataset for development of Polish Question Answering systems}, | |
author={Marcinczuk, Michal and Ptak, Marcin and Radziszewski, Adam and Piasecki, Maciej}, | |
booktitle={Proceedings of the 6th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, Wydawnictwo Poznanskie, Fundacja Uniwersytetu im. Adama Mickiewicza}, | |
year={2013} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The Did You Know (pol. Czy wiesz?) dataset consists of human-annotated question-answer pairs. The task is to predict if the answer is correct. We chose the negatives which have the largest token overlap with a question. | |
""" | |
_HOMEPAGE = "http://nlp.pwr.wroc.pl/en/tools-and-resources/resources/czy-wiesz-question-answering-dataset" | |
_LICENSE = "CC BY-SA 3.0" | |
_URLs = "https://klejbenchmark.com/static/data/klej_dyk.zip" | |
class DYK(datasets.GeneratorBasedBuilder): | |
"""Did You Know? Dataset""" | |
VERSION = datasets.Version("1.1.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"q_id": datasets.Value("string"), | |
"question": datasets.Value("string"), | |
"answer": datasets.Value("string"), | |
"target": datasets.ClassLabel(names=["0", "1"]), | |
} | |
), | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
data_dir = dl_manager.download_and_extract(_URLs) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "train.tsv"), | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={"filepath": os.path.join(data_dir, "test_features.tsv"), "split": "test"}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
""" Yields examples. """ | |
with open(filepath, encoding="utf-8") as f: | |
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE) | |
for id_, row in enumerate(reader): | |
yield id_, { | |
"q_id": row["q_id"], | |
"question": row["question"], | |
"answer": row["answer"], | |
"target": -1 if split == "test" else row["target"], | |
} | |