Datasets:
kor_3i4k

	# 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}