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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
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