Datasets:
Tasks:
Text Classification
Languages:
Korean
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
License:
File size: 2,811 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 Sarcasm Detection Dataset"""
import csv
import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """\
This is a dataset designed to detect sarcasm in Korean because it distorts the literal meaning of a sentence
and is highly related to sentiment classification.
"""
_HOMEPAGE = "https://github.com/SpellOnYou/korean-sarcasm"
_LICENSE = "MIT License"
_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/SpellOnYou/korean-sarcasm/master/data/jiwon/train.csv"
_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/SpellOnYou/korean-sarcasm/master/data/jiwon/test.csv"
class KorSarcasm(datasets.GeneratorBasedBuilder):
"""Korean Sarcasm Detection Dataset"""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"tokens": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["no_sarcasm", "sarcasm"]),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
task_templates=[TextClassification(text_column="tokens", label_column="label")],
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
"""Generate Korean sarcasm examples"""
with open(filepath, encoding="utf-8") as csv_file:
data = csv.reader(csv_file, delimiter=",")
next(data, None)
for id_, row in enumerate(data):
row = row[1:3]
tokens, label = row
yield id_, {"tokens": tokens, "label": int(label)}
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