Datasets:

License:
xlm-r-bertic-data / xlm-r-bertic-data.py
5roop's picture
Rename bertic_data to xlm-r-bertic-data
a938bc2
import datasets
import gzip
import os
from typing import List
_URL = "http://nl.ijs.si/nikola/dedup_hbs/"
_URLS = {
"macocu_hbs": _URL + "macocu.hbs.translit.dedup.lines.gz",
"hr_news": _URL + "hr_news.translit.dedup.lines.gz",
"bswac": _URL + "bswac.translit.dedup.lines.gz",
"cc100_hr": _URL + "cc100-hr.translit.dedup.lines.gz",
"cc100_sr": _URL + "cc100-sr.translit.dedup.lines.gz",
"classla_sr": _URL + "classla-sr.translit.dedup.lines.gz",
"classla_hr": _URL + "classla-hr.translit.dedup.lines.gz",
"classla_bs": _URL + "classla-bs.translit.dedup.lines.gz",
"cnrwac": _URL + "cnrwac.translit.dedup.lines.gz",
"hrwac": _URL + "hrwac.translit.dedup.lines.gz",
"mC4": _URL + "mC4.sr.translit.dedup.lines.gz",
"riznica": _URL + "riznica.translit.dedup.lines.gz",
"srwac": _URL + "srwac.translit.dedup.lines.gz",
}
_HOMEPAGE = _URL
_DESCRIPTION = """\
Data used to train XLM-Roberta-Bertić.
"""
_CITATION = r"""
To be added soon."""
class BerticData(datasets.GeneratorBasedBuilder):
"""Bertic dataset, used for training Bertic model."""
VERSION = datasets.Version("1.0.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from BerticDataConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
def _info(self):
features = datasets.Features(
{
"text": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=url,
gen_kwargs={"filepath": downloaded_files[url]},
)
for url in _URLS
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
key = 0
for name in [filepath]:
# with gzip.open(name, "rb") as f:
with open(name, "r") as f:
for line in f.readlines():
yield key, {"text": line.rstrip()}
key += 1