Convert dataset to Parquet (#4)

- Convert dataset to Parquet (faf9bb39db59a06055a39f7b8cb2f737067158d1)
- Delete loading script (4ae4e65e2e2a2c5ad7a8952fbfbe01a65bb470db)

README.md

@@ -35,13 +35,20 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 3102158
+    num_bytes: 3102134
     num_examples: 55134
   - name: test
-    num_bytes: 344028
+    num_bytes: 344024
     num_examples: 6121
-  download_size: 2956114
-  dataset_size: 3446186
+  download_size: 1974323
+  dataset_size: 3446158
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for 3i4K

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:90fdc098e3053288ead52b64a703dcb5f401dd1f32d6eee6fbd36f7df072461b
+size 200394

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c63c2775ae44f668be76a36799974ee3bd0c83e5af343e3bd6c57ce3dc0c4e3
+size 1773929

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}