Datasets:
Tasks:
Text Classification
Sub-tasks:
semantic-similarity-classification
Languages:
Korean
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
other
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 3,375 Bytes
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# 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.
"""Korean pairwise question binary classification dataset"""
import datasets
_CITATION = """\
@misc{Song:2018,
title = "Paired Question v.2",
authors = "Youngsook Song",
publisher = "GitHub",
year = "2018"
}
"""
_DESCRIPTION = """\
This is a Korean paired question dataset containing labels indicating whether two questions in a given pair are semantically identical. This dataset was used to evaluate the performance of [KoGPT2](https://github.com/SKT-AI/KoGPT2#subtask-evaluations) on a phrase detection downstream task.
"""
_HOMEPAGE = "https://github.com/songys/Question_pair"
_LICENSE = "The MIT License (MIT)"
_URL = "https://raw.githubusercontent.com/songys/Question_pair/master/"
_URLs = {key: f"{_URL}{key}.txt" for key in ("train", "test", "validation")}
class KorQpair(datasets.GeneratorBasedBuilder):
"""Korean pairwise question classification dataset"""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"question1": datasets.Value("string"),
"question2": datasets.Value("string"),
"is_duplicate": datasets.ClassLabel(names=["0", "1"]),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLs)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": downloaded_files["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": downloaded_files["test"],
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": downloaded_files["validation"],
"split": "validation",
},
),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
next(f)
for id_, row in enumerate(f):
row = row.strip().split("\t")
yield id_, {
"question1": row[0],
"question2": row[1],
"is_duplicate": row[2],
}
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