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import datasets |
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import json |
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import os |
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import sys |
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dl = datasets.DownloadManager() |
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configs_file = dl.download('https://huggingface.co/datasets/RealTimeData/bbc_alltime/raw/main/configs.txt') |
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with open(configs_file, encoding="utf-8") as f: |
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_TIMES = f.read().splitlines() |
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_CITATION = """\ |
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@misc{li2023estimating, |
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title={Estimating Contamination via Perplexity: Quantifying Memorisation in Language Model Evaluation}, |
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author={Yucheng Li}, |
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year={2023}, |
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eprint={2309.10677}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """\ |
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This dataset contains BBC News articles from 2017 to 2022. The articles are arraged by month. Access the specific month by using the format "YYYY-MM" as config. Such as load_dataset("RealTimeData/bbc_alltime", "2021-1"). |
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""" |
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_HOMEPAGE = "https://github.com/liyucheng09/Contamination_Detector" |
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class Bbc_alltimes(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name=time, version=datasets.Version("1.0.0"), description=f"BBC News articles published in the priod of {time}" |
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) |
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for time in _TIMES |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"title": datasets.Value("string"), |
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"published_date": datasets.Value("string"), |
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"authors": datasets.Value("string"), |
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"description": datasets.Value("string"), |
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"section": datasets.Value("string"), |
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"content": datasets.Value("string"), |
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"link": datasets.Value("string"), |
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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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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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if self.config.name == "all": |
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times = _TIMES[:-1] |
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files = dl_manager.download_and_extract('all_articles.zip') |
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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={"files": files}, |
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) |
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] |
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else: |
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time = self.config.name |
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_URL = f"articles/{time}.json" |
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file = dl_manager.download(_URL) |
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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={"files": file}, |
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) |
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] |
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def _generate_examples(self, files): |
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"""Yields examples.""" |
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if self.config.name == "all": |
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assert isinstance(files, list) |
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for file in files: |
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time = file.strip('.json') |
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with open(file, encoding="utf-8") as f: |
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data = json.load(f) |
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length = len(data['title']) |
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for i in range(length): |
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yield f'{time}-{i}', { |
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"title": data['title'][i], |
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"published_date": data['published_date'][i], |
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"authors": data['authors'][i], |
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"description": data['description'][i], |
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"section": data['section'][i], |
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"content": data['content'][i], |
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"link": data['link'][i], |
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} |
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else: |
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assert isinstance(files, str) |
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time = self.config.name |
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with open(files, encoding="utf-8") as f: |
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data = json.load(f) |
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length = len(data['title']) |
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for i in range(length): |
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yield f'{time}-{i}', { |
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"title": data['title'][i], |
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"published_date": data['published_date'][i], |
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"authors": data['authors'][i], |
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"description": data['description'][i], |
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"section": data['section'][i], |
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"content": data['content'][i], |
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"link": data['link'][i], |
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} |