import logging import math import pathlib import random from dataclasses import dataclass from typing import Dict, List import datasets as ds logger = logging.getLogger(__name__) _CITATION = """\ https://www.rondhuit.com/download.html#ldcc """ _DESCRIPTION = """\ 本コーパスは、NHN Japan株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限りHTMLタグを取り除いて作成したものです。 """ _HOMEPAGE = "https://www.rondhuit.com/download.html#ldcc" _LICENSE = """\ 各記事ファイルにはクリエイティブ・コモンズライセンス「表示 – 改変禁止」が適用されます。 クレジット表示についてはニュースカテゴリにより異なるため、ダウンロードしたファイルを展開したサブディレクトリにあるそれぞれの LICENSE.txt をご覧ください。 livedoor はNHN Japan株式会社の登録商標です。 """ _DOWNLOAD_URL = "https://www.rondhuit.com/download/ldcc-20140209.tar.gz" @dataclass class LivedoorNewsCorpusConfig(ds.BuilderConfig): train_ratio: float = 0.8 val_ratio: float = 0.1 test_ratio: float = 0.1 shuffle: bool = False random_state: int = 0 def __post_init__(self): assert self.train_ratio + self.val_ratio + self.test_ratio == 1.0 class LivedoorNewsCorpusDataset(ds.GeneratorBasedBuilder): VERSION = ds.Version("1.0.0") BUILDER_CONFIG_CLASS = LivedoorNewsCorpusConfig BUILDER_CONFIGS = [ LivedoorNewsCorpusConfig( version=VERSION, description="Livedoor ニュースコーパス", ) ] def _info(self) -> ds.DatasetInfo: features = ds.Features( { "url": ds.Value("string"), "date": ds.Value("string"), "title": ds.Value("string"), "content": ds.Value("string"), "category": ds.ClassLabel( names=[ "movie-enter", "it-life-hack", "kaden-channel", "topic-news", "livedoor-homme", "peachy", "sports-watch", "dokujo-tsushin", "smax", ] ), } ) return ds.DatasetInfo( description=_DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, license=_LICENSE, features=features, ) def _split_generators(self, dl_manager: ds.DownloadManager): dataset_root = dl_manager.download_and_extract(_DOWNLOAD_URL) dataset_root_dir = pathlib.Path(dataset_root) / "text" article_paths = list(dataset_root_dir.glob("*/**/*.txt")) article_paths = list(filter(lambda p: p.name != "LICENSE.txt", article_paths)) config: LivedoorNewsCorpusConfig = self.config if config.shuffle: random.seed(config.random_state) random.shuffle(article_paths) num_articles = len(article_paths) num_tng = math.ceil(num_articles * config.train_ratio) num_val = math.ceil(num_articles * config.val_ratio) num_tst = math.ceil(num_articles * config.test_ratio) tng_articles = article_paths[:num_tng] val_articles = article_paths[num_tng : num_tng + num_val] tst_articles = article_paths[num_tng + num_val : num_tng + num_val + num_tst] assert len(tng_articles) + len(val_articles) + len(tst_articles) == num_articles return [ ds.SplitGenerator( name=ds.Split.TRAIN, # type: ignore gen_kwargs={"article_paths": tng_articles}, ), ds.SplitGenerator( name=ds.Split.VALIDATION, # type: ignore gen_kwargs={"article_paths": val_articles}, ), ds.SplitGenerator( name=ds.Split.TEST, # type: ignore gen_kwargs={"article_paths": tst_articles}, ), ] def parse_article(self, article_data: List[str]) -> Dict[str, str]: article_url = article_data[0] article_date = article_data[1] article_title = article_data[2] article_content = " ".join(article_data[3:]) example_dict = { "url": article_url, "date": article_date, "title": article_title, "content": article_content, } return example_dict def _generate_examples(self, article_paths: List[pathlib.Path]): # type: ignore[override] for i, article_path in enumerate(article_paths): article_category = article_path.parent.name with open(article_path, "r") as rf: article_data = [line.strip() for line in rf] example_dict = self.parse_article(article_data=article_data) example_dict["category"] = article_category yield i, example_dict