Datasets:
Languages:
Sanskrit
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
Tags:
License:
File size: 2,776 Bytes
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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""
import os
import datasets
_CITATION = """\
@Misc{johnsonetal2014,
author = {Johnson, Kyle P. and Patrick Burns and John Stewart and Todd Cook},
title = {CLTK: The Classical Language Toolkit},
url = {https://github.com/cltk/cltk},
year = {2014--2020},
}
"""
_DESCRIPTION = """\
This dataset combines some of the classical Sanskrit texts.
"""
_HOMEPAGE = "https://github.com/parmarsuraj99/hf_datasets/tree/master/sanskrit_classic"
_LICENSE = ""
_URLs = {
"combined": "https://github.com/parmarsuraj99/hf_datasets/raw/master/sanskrit_classic/combined.zip",
}
class SanskritClassic(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="combined", version=VERSION, description="This is combined version of classical texts"
),
]
def _info(self):
features = datasets.Features(
{
"text": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
my_urls = _URLs[self.config.name]
data_dir = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": os.path.join(data_dir, "combined.txt"),
"split": "train",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
yield id_, {
"text": row,
}
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