tajik-text-segmentation.py

# 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,
            }