Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
Turkish
Size:
< 1K
License:
Create data.py
Browse files
data.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""datas."""
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import csv
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import datasets
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from datasets.tasks import TextClassification
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_CITATION = """\
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@inproceedings{Casanueva2020,
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author = pnr,
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title = {sentiment},
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year = {2022},
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month = {mar},
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note = {Data available at https://github.com/PnrSvc/dataset},
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url = {a},
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booktitle = {a}
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}
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"""
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_DESCRIPTION = """\
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description
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"""
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_HOMEPAGE = "https://github.com/PnrSvc/dataset"
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_TRAIN_DOWNLOAD_URL = (
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"https://github.com/PnrSvc/dataset/blob/main/turkish/train.csv"
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)
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_TEST_DOWNLOAD_URL = "https://github.com/PnrSvc/dataset/blob/main/turkish/test.csv"
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class Datas(datasets.GeneratorBasedBuilder):
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"""datas dataset."""
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VERSION = datasets.Version("1.1.0")
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def _info(self):
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features = datasets.Features(
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{
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"label": datasets.Value("string"),
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"target": datasets.features.ClassLabel(
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names=[
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"negative",
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"neutral",
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"positive"
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]
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),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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citation=_CITATION,
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task_templates=[TextClassification(text_column="label", label_column="target")],
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
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test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
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]
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def _generate_examples(self, filepath):
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"""Yields examples as (key, example) tuples."""
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with open(filepath, encoding="utf-8") as f:
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csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
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# call next to skip header
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next(csv_reader)
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for id_, row in enumerate(csv_reader):
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label, target = row
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yield id_, {"text": label, "label": target}
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