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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""Arabic Vocalized Words Dataset."""
import glob
import os
import datasets
_DESCRIPTION = """\
Arabic vocalized texts.
it contains 75 million of fully vocalized words mainly\
97 books from classical and modern Arabic language.
"""
_CITATION = """\
@article{zerrouki2017tashkeela,
title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems},
author={Zerrouki, Taha and Balla, Amar},
journal={Data in brief},
volume={11},
pages={147},
year={2017},
publisher={Elsevier}
}
"""
_HOMEPAGE = "https://sourceforge.net/projects/tashkeela/"
_LICENSE = "GPLv2"
_DOWNLOAD_URL = "https://sourceforge.net/projects/tashkeela/files/latest/download"
class TashkeelaConfig(datasets.BuilderConfig):
"""BuilderConfig for Tashkeela."""
def __init__(self, **kwargs):
"""BuilderConfig for Tashkeela.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TashkeelaConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
class Tashkeela(datasets.GeneratorBasedBuilder):
"""Tashkeela dataset."""
BUILDER_CONFIGS = [
TashkeelaConfig(
name="plain_text",
description="Plain text",
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"book": datasets.Value("string"),
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
arch_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"directory": os.path.join(arch_path, "Tashkeela-arabic-diacritized-text-utf8-0.3", "texts.txt")
},
),
]
def _generate_examples(self, directory):
"""Generate examples."""
for id_, file_name in enumerate(sorted(glob.glob(os.path.join(directory, "**.txt")))):
with open(file_name, encoding="UTF-8") as f:
yield str(id_), {"book": file_name, "text": f.read().strip()}
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