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delete old dataset loading script

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  1. tydip.py +0 -118
tydip.py DELETED
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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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-
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- # Lint as: python3
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- """TyDiP: A Multilingual Politeness Dataset"""
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-
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-
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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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-
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-
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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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-
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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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-
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- _LANGUAGES = ("en", "hi", "ko", "es", "ta", "fr", "vi", "ru", "af", "hu")
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-
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-
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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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-
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-
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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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-
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-
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- class MultilingualLibrispeech(datasets.GeneratorBasedBuilder):
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- """TyDiP dataset."""
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-
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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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-
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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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-
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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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-
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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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- }