Delete loading script

kor_3i4k.py

@@ -1,95 +0,0 @@
-# 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}