telugu_books.py

# 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.
"""Telugu Books Dataset"""


import csv
import os

import datasets


_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Indic NLP - Natural Language Processing for Indian Languages},
authors = {Sudalai Rajkumar, Anusha Motamarri},
year={2019}
}
"""

_DESCRIPTION = """\
This dataset is created by scraping telugu novels from teluguone.com this dataset can be used for nlp tasks like topic modeling, word embeddings, transfer learning etc
"""

_HOMEPAGE = "https://www.kaggle.com/sudalairajkumar/telugu-nlp"

_LICENSE = "Data files © Original Authors"

_FILENAME = "telugu_books.csv"


class TeluguBooks(datasets.GeneratorBasedBuilder):
    """Telugu novels"""

    VERSION = datasets.Version("1.1.0")

    @property
    def manual_download_instructions(self):
        return """\
    You need to go to https://www.kaggle.com/sudalairajkumar/telugu-nlp,
    and manually download the telugu_books. Once it is completed,
    a file named telugu_books.zip will be appeared in your Downloads folder
    or whichever folder your browser chooses to save files to. You then have
    to unzip the file and move telugu_books,csv under <path/to/folder>.
    The <path/to/folder> can e.g. be "~/manual_data".
    telugu_books can then be loaded using the following command `datasets.load_dataset("telugu_books", data_dir="<path/to/folder>")`.
    """

    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."""
        path_to_manual_file = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
        if not os.path.exists(path_to_manual_file):
            raise FileNotFoundError(
                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('telugu_books', data_dir=...)` that includes file name {}. Manual download instructions: {}".format(
                    path_to_manual_file,
                    _FILENAME,
                    self.manual_download_instructions,
                )
            )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(path_to_manual_file, "telugu_books.csv"),
                    "split": "train",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(csv_file)
            for id_, row in enumerate(csv_reader):
                _, text = row
                yield id_, {"text": text}