import os from pathlib import Path from typing import Dict, List, Tuple import datasets from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """\ @inproceedings{cruz2021exploiting, title={Exploiting news article structure for automatic corpus generation of entailment datasets}, author={Cruz, Jan Christian Blaise and Resabal, Jose Kristian and Lin, James and Velasco, Dan John and Cheng, Charibeth}, booktitle={PRICAI 2021: Trends in Artificial Intelligence: 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, November 8--12, 2021, Proceedings, Part II 18}, pages={86--99}, year={2021}, organization={Springer} } """ _DATASETNAME = "newsph" _LANGUAGES = ["fil", "tgl"] _DESCRIPTION = """\ Raw collection of news articles in Filipino which can be used for language modelling. """ _HOMEPAGE = "https://huggingface.co/datasets/newsph" _LICENSE = Licenses.GPL_3_0.value _LOCAL = False _URLS = "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/newsph/newsph.zip" _SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class NewsPhDataset(datasets.GeneratorBasedBuilder): """ Raw collection of news articles in Filipino which can be used for language modelling. """ SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ SEACrowdConfig( name="newsph_source", version=SOURCE_VERSION, description="newsph source schema", schema="source", subset_id="newsph", ), SEACrowdConfig( name="newsph_seacrowd_ssp", version=SEACROWD_VERSION, description="newsph SEACrowd schema", schema="seacrowd_ssp", subset_id="newsph", ), ] DEFAULT_CONFIG_NAME = "newsph_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "id": datasets.Value("string"), "text": datasets.Value("string"), } ) elif self.config.schema == "seacrowd_ssp": features = schemas.self_supervised_pretraining.features else: raise NotImplementedError(f"Schema '{self.config.schema}' is not defined.") return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" data_dir = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "filepath": os.path.join(data_dir, "newsph", "train.txt"), "split": "train", }, ), ] def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" if self.config.schema == "source" or self.config.schema == "seacrowd_ssp": with open(filepath, encoding="utf-8") as f: for idx, row in enumerate(f): if row.strip(): yield idx, {"id": str(idx), "text": row} else: yield idx, {"id": str(idx), "text": ""} else: raise NotImplementedError