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# 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.
from .annotations_parser import load_yedda_annotations
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@misc{tajik-text-segmentation,
title = {Tajik text segmentation dataset},
author={Sobir Bobiev},
year={2023}
}
"""
_DESCRIPTION = """\
This dataset contains tajik texts with sentences annotated. Can be useful for sentence boundary detection, segmenting text and many NLP tasks.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
class TajikTextSegmentation(datasets.GeneratorBasedBuilder):
"""A dataset of sentence-wise text segmentation in Tajik language."""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"file": datasets.Value("string"),
"text": datasets.Value("string"),
"annotated_text": datasets.Value("string"),
"number_of_labels": datasets.Value("int32"),
"positions": [[datasets.Value("int32")]],
"labels": [datasets.Value("string")]
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"directory_path": './annotations',
},
),
]
def _generate_examples(self, directory_path):
"""This function returns the examples."""
annotations = load_yedda_annotations(directory_path)
for idx, file_annotation in enumerate(annotations):
file = file_annotation['file']
text = file_annotation['text']
annotated_text = file_annotation['annotated_text']
number_of_labels = len(file_annotation['labels'])
yield idx, {
"file": file,
"text": text,
"annotated_text": annotated_text,
"positions": file_annotation['positions'],
"labels": file_annotation['labels'],
"number_of_labels": number_of_labels,
}
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