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from pathlib import Path |
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from typing import List |
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import datasets |
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import pandas as pd |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import DEFAULT_SEACROWD_VIEW_NAME, DEFAULT_SOURCE_VIEW_NAME, Tasks |
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_DATASETNAME = "id_hoax_news" |
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_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME |
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_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME |
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_LANGUAGES = ["ind"] |
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_LOCAL = False |
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_CITATION = """\ |
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@INPROCEEDINGS{8265649, author={Pratiwi, Inggrid Yanuar Risca and Asmara, Rosa Andrie and Rahutomo, Faisal}, booktitle={2017 11th International Conference on Information & Communication Technology and System (ICTS)}, title={Study of hoax news detection using naïve bayes classifier in Indonesian language}, year={2017}, volume={}, number={}, pages={73-78}, doi={10.1109/ICTS.2017.8265649}} |
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""" |
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_DESCRIPTION = """\ |
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This research proposes to build an automatic hoax news detection and collects 250 pages of hoax and valid news articles in Indonesian language. |
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Each data sample is annotated by three reviewers and the final taggings are obtained by voting of those three reviewers. |
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""" |
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_HOMEPAGE = "https://data.mendeley.com/datasets/p3hfgr5j3m/1" |
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_LICENSE = "Creative Commons Attribution 4.0 International" |
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_URLs = { |
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"train": "https://data.mendeley.com/public-files/datasets/p3hfgr5j3m/files/38bfcff2-8a32-4920-9c26-4f63b5b2dad8/file_downloaded", |
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} |
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_SUPPORTED_TASKS = [Tasks.HOAX_NEWS_CLASSIFICATION] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class IdHoaxNews(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name="id_hoax_news_source", |
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version=datasets.Version(_SOURCE_VERSION), |
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description="Hoax News source schema", |
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schema="source", |
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subset_id="id_hoax_news", |
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), |
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SEACrowdConfig( |
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name="id_hoax_news_seacrowd_text", |
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version=datasets.Version(_SEACROWD_VERSION), |
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description="Hoax News Nusantara schema", |
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schema="seacrowd_text", |
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subset_id="id_hoax_news", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "id_hoax_news_source" |
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def _info(self): |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"index": datasets.Value("string"), |
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"news": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "seacrowd_text": |
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features = schemas.text_features(["Valid", "Hoax"]) |
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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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train_tsv_path = Path(dl_manager.download_and_extract(_URLs["train"])) |
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data_files = { |
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"train": train_tsv_path / "250 news with valid hoax label.csv", |
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} |
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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": data_files["train"]}, |
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), |
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] |
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def _generate_examples(self, filepath: Path): |
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news_file = open(filepath, 'r', encoding='ISO-8859-1') |
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lines = news_file.readlines() |
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news = [] |
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labels = [] |
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curr_news = '' |
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for l in lines[1:]: |
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l = l.replace('\n', '') |
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if ';Valid' in l: |
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curr_news += l.replace(';Valid', '') |
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news.append(curr_news) |
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labels.append('Valid') |
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curr_news = '' |
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elif ';Hoax' in l: |
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curr_news += l.replace(';Hoax', '') |
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news.append(curr_news) |
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labels.append('Hoax') |
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curr_news = '' |
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else: |
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curr_news += l + ' ' |
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if self.config.schema == "source": |
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for i in range(len(news)): |
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ex = {"index": str(i), "news": news[i], "label": labels[i]} |
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yield i, ex |
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elif self.config.schema == "seacrowd_text": |
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for i in range(len(news)): |
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ex = {"id": str(i), "text": news[i], "label": labels[i]} |
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yield i, ex |
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else: |
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raise ValueError(f"Invalid config: {self.config.name}") |
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