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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import datasets |
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import pandas as pd |
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_URLS = { |
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"annotated_data": "https://huggingface.co/datasets/Exqrch/IndoToxic2024/resolve/main/indotoxic2024_annotated_data.csv" |
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} |
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_DESCRIPTION = """\ |
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IndoToxic2024 is an Indonesian datasets collected prior and during the 2024 Indonesia's presidential election. |
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The data are obtained from social media and are annotated by 19 annotators of diverse background. |
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The tasks supported by this datasets are text classification task around hate speech and toxic content. |
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""" |
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_HOMEPAGE = """\ |
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https://github.com/izzako/IndoToxic2024 |
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""" |
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_LICENSE = """\ |
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Apache License 2.0 |
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""" |
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_CITATION = """\ |
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Susanto, L., Wijanarko, M. I., Pratama, P. A., Hong, T., Idris, I., Aji, A. F., & Wijaya, D. (2024, June 27). IndoToxic2024: A Demographically-Enriched Dataset of Hate Speech and Toxicity Types for Indonesian Language. arXiv.org. https://arxiv.org/abs/2406.19349 |
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""" |
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class IndoToxic2024(datasets.GeneratorBasedBuilder): |
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def _info(self) -> datasets.DatasetInfo: |
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feature = { |
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'batch_id': datasets.Value("string"), |
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'batch_text_id': datasets.Value("string"), |
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'text_id': datasets.Value("string"), |
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'metadata_id': datasets.Value("string"), |
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'annotator_id': datasets.Value("string"), |
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'text': datasets.Value("string"), |
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'initial_paragraph': datasets.Value("string"), |
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'topic': datasets.Value("string"), |
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'is_noise_or_spam_text': datasets.Value("int32"), |
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'related_to_election_2024': datasets.Value("int32"), |
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'toxicity': datasets.Value('int32'), |
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'profanity_obscenity': datasets.Value('int32'), |
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'threat_incitement_to_violence': datasets.Value('int32'), |
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'insults': datasets.Value('int32'), |
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'identity_attack': datasets.Value('int32'), |
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'sexually_explicit': datasets.Value('int32') |
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} |
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features = datasets.Features(features) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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train_csv_path = Path(dl_manager.download_and_extract(_URLS["annotated_data"])) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": train_csv_path}, |
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), |
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] |
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: |
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df = pd.read_csv(filepath).reset_index() |
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for index, row in df.iterrows(): |
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ex = { |
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'batch_id': row['batch_id'], |
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'batch_text_id': row['batch_text_id'], |
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'text_id': row['text_id'], |
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'metadata_id': row['metadata_id'], |
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'annotator_id': row['annotator_id'], |
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'text': row['text'], |
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'initial_paragraph': row['initial_paragraph'], |
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'topic': row['topic'], |
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'is_noise_or_spam_text': row['is_noise_or_spam_text'], |
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'related_to_election_2024': row['related_to_election_2024'], |
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'toxicity': row['toxicity'], |
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'profanity_obscenity': row['profanity_obscenity'], |
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'threat_incitement_to_violence': row['threat_incitement_to_violence'], |
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'insults': row['insults'], |
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'identity_attack': row['identity_attack'], |
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'sexually_explicit': row['sexually_explicit'] |
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} |
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yield str(index), ex |
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