Datasets:
telugu_books

	# 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
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	"""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(
	f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('telugu_books', data_dir=...)` that includes file name {_FILENAME}. Manual download instructions: {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}