Datasets:
Tasks:
Text Classification
Sub-tasks:
intent-classification
Languages:
Korean
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 3,579 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.
"""3i4K: Intonation-aided intention identification for Korean dataset"""
import csv
import datasets
from datasets.tasks import TextClassification
_CITATION = """\
@article{cho2018speech,
title={Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency},
author={Cho, Won Ik and Lee, Hyeon Seung and Yoon, Ji Won and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1811.04231},
year={2018}
}
"""
_DESCRIPTION = """\
This dataset is designed to identify speaker intention based on real-life spoken utterance in Korean into one of
7 categories: fragment, description, question, command, rhetorical question, rhetorical command, utterances.
"""
_HOMEPAGE = "https://github.com/warnikchow/3i4k"
_LICENSE = "CC BY-SA-4.0"
_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/warnikchow/3i4k/master/data/train_val_test/fci_train_val.txt"
_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/warnikchow/3i4k/master/data/train_val_test/fci_test.txt"
class Kor_3i4k(datasets.GeneratorBasedBuilder):
"""Intonation-aided intention identification for Korean"""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"label": datasets.features.ClassLabel(
names=[
"fragment",
"statement",
"question",
"command",
"rhetorical question",
"rhetorical command",
"intonation-dependent utterance",
]
),
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=[TextClassification(text_column="text", 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):
"""Generates 3i4K examples"""
with open(filepath, encoding="utf-8") as csv_file:
data = csv.reader(csv_file, delimiter="\t")
for id_, row in enumerate(data):
label, text = row
yield id_, {"label": int(label), "text": text}
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