Upload wikitext_tl_39.py with huggingface_hub

wikitext_tl_39.py

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+import os
+from pathlib import Path
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@article{cruz2019evaluating,
+  title={Evaluating Language Model Finetuning Techniques for Low-resource Languages},
+  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
+  journal={arXiv preprint arXiv:1907.00409},
+  year={2019}
+}
+"""
+
+_DATASETNAME = "wikitext_tl_39"
+
+_DESCRIPTION = """A benchmark Language Modeling dataset for Tagalog. The dataset construction was done similar to that of the WikiText
+Long Term Dependency Language Modeling Dataset, with a some differences, such as in how Wikipedia was scraped and how the vocabulary was
+created. The dataset contains 39 Million tokens in the training set.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/wikitext_tl39"
+
+_LANGUAGES = ["fil"]
+
+_LICENSE = Licenses.GPL_3_0.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://s3.us-east-2.amazonaws.com/blaisecruz.com/datasets/wikitext-tl-39/wikitext-tl-39.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class WikiTextTL39Dataset(datasets.GeneratorBasedBuilder):
+    """Large scale, unlabeled text dataset with 39 Million tokens in the training set in Tagalog."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_ssp",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_ssp",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        features = schemas.ssp_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> list[datasets.SplitGenerator]:
+        data_dir = dl_manager.download_and_extract(_URLS[_DATASETNAME])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(data_dir, "wikitext-tl-39", "train.txt"), "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, "wikitext-tl-39", "test.txt"), "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": os.path.join(data_dir, "wikitext-tl-39", "valid.txt"), "split": "valid"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> tuple[int, dict]:
+        with open(filepath, encoding="utf-8") as f:
+            for i, row in enumerate(f):
+                if row.strip():
+                    yield i, {
+                        "id": str(i),
+                        "text": row,
+                    }
+                else:
+                    yield i, {
+                        "id": str(i),
+                        "text": "",
+                    }