| 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 | |