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newsph.py

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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