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"""The CC-News dataset is based on Common Crawl News Dataset by Sebastian Nagel""" |
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import json |
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import os |
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from fnmatch import fnmatch |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_DESCRIPTION = """\ |
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CC-News containing news articles from news sites all over the world \ |
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The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/. \ |
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This version of the dataset has 708241 articles. It represents a small portion of English \ |
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language subset of the CC-News dataset created using news-please(Hamborg et al.,2017) to \ |
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collect and extract English language portion of CC-News. |
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""" |
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_CITATION = """\ |
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@InProceedings{Hamborg2017, |
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author = {Hamborg, Felix and Meuschke, Norman and Breitinger, Corinna and Gipp, Bela}, |
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title = {news-please: A Generic News Crawler and Extractor}, |
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year = {2017}, |
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booktitle = {Proceedings of the 15th International Symposium of Information Science}, |
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location = {Berlin}, |
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doi = {10.5281/zenodo.4120316}, |
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pages = {218--223}, |
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month = {March} |
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} |
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""" |
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_PROJECT_URL = "https://commoncrawl.org/2016/10/news-dataset-available/" |
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_DOWNLOAD_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/cc_news/cc_news.tar.gz" |
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class CCNewsConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CCNews.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for CCNews. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(CCNewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs) |
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class CCNews(datasets.GeneratorBasedBuilder): |
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"""CC-News dataset.""" |
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BUILDER_CONFIGS = [ |
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CCNewsConfig( |
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name="plain_text", |
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description="Plain text", |
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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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"title": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"domain": datasets.Value("string"), |
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"date": datasets.Value("string"), |
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"description": datasets.Value("string"), |
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"url": datasets.Value("string"), |
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"image_url": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_PROJECT_URL, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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archive = dl_manager.download(_DOWNLOAD_URL) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive)}), |
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] |
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def _generate_examples(self, files): |
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id_ = 0 |
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for article_file_path, f in files: |
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if fnmatch(os.path.basename(article_file_path), "*.json"): |
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article = json.load(f) |
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yield id_, { |
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"title": article["title"].strip() if article["title"] is not None else "", |
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"text": article["maintext"].strip() if article["maintext"] is not None else "", |
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"domain": article["source_domain"].strip() if article["source_domain"] is not None else "", |
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"date": article["date_publish"].strip() if article["date_publish"] is not None else "", |
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"description": article["description"].strip() if article["description"] is not None else "", |
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"url": article["url"].strip() if article["url"] is not None else "", |
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"image_url": article["image_url"].strip() if article["image_url"] is not None else "", |
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} |
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id_ += 1 |
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