newsph.py

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