File size: 3,668 Bytes
75f70f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
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": "",
}
|