Datasets:
Genius1237
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delete old dataset loading script
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tydip.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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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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# Lint as: python3
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"""TyDiP: A Multilingual Politeness Dataset"""
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import csv
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from dataclasses import dataclass
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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{srinivasan-choi-2022-tydip,
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title = "{T}y{D}i{P}: A Dataset for Politeness Classification in Nine Typologically Diverse Languages",
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author = "Srinivasan, Anirudh and
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Choi, Eunsol",
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booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
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month = dec,
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year = "2022",
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address = "Abu Dhabi, United Arab Emirates",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2022.findings-emnlp.420",
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pages = "5723--5738",
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}"""
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_DESCRIPTION = """\
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The TyDiP dataset is a dataset of requests in conversations between wikipedia editors
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that have been annotated for politeness. The splits available below consists of only
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requests from the top 25 percentile (polite) and bottom 25 percentile (impolite) of
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politeness scores. The English train set and English test set that are
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adapted from the Stanford Politeness Corpus, and test data in 9 more languages
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(Hindi, Korean, Spanish, Tamil, French, Vietnamese, Russian, Afrikaans, Hungarian)
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was annotated by us.
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"""
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_LANGUAGES = ("en", "hi", "ko", "es", "ta", "fr", "vi", "ru", "af", "hu")
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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# _URL = "https://huggingface.co/datasets/Genius1237/TyDiP/resolve/main/data/binary/"
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_URL = "https://huggingface.co/datasets/Genius1237/TyDiP/raw/main/data/binary/"
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_URLS = {
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'en': {
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'train': _URL + 'en_train_binary.csv',
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'test': _URL + 'en_test_binary.csv'
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},
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} | {lang: {'test': _URL + '{}_test_binary.csv'.format(lang)} for lang in _LANGUAGES[1:]}
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@dataclass
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class TyDiPConfig(datasets.BuilderConfig):
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"""BuilderConfig for TyDiP."""
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lang: str = None
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class MultilingualLibrispeech(datasets.GeneratorBasedBuilder):
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"""TyDiP dataset."""
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BUILDER_CONFIGS = [
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TyDiPConfig(name=lang, lang=lang) for lang in _LANGUAGES
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"text": datasets.Value("string"),
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"labels": datasets.ClassLabel(num_classes=2, names=[0, 1]),
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}
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),
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supervised_keys=("text", "labels"),
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homepage=_URL,
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citation=_CITATION,
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task_templates=[TextClassification(text_column="text", label_column="labels")],
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)
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def _split_generators(self, dl_manager):
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splits = []
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if self.config.lang == 'en':
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file_path = dl_manager.download_and_extract(_URLS['en']['train'])
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splits.append(
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN, gen_kwargs={"data_file": file_path}
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))
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file_path = dl_manager.download_and_extract(_URLS[self.config.lang]['test'])
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splits.append(
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datasets.SplitGenerator(
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name=datasets.Split.TEST, gen_kwargs={"data_file": file_path}
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)
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)
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return splits
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def _generate_examples(self, data_file):
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"""Generate examples from a TyDiP data file"""
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with open(data_file) as f:
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csv_reader = csv.reader(f)
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for i, row in enumerate(csv_reader):
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if i != 0:
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yield i - 1, {
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"text": row[0],
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"labels": int(float(row[1]) > 0),
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}
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