Datasets:
Languages:
Telugu
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
other
Annotations Creators:
machine-generated
Source Datasets:
original
License:
# 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. | |
"""TODO: Add a description here.""" | |
import csv | |
import os | |
import datasets | |
_CITATION = """\ | |
@InProceedings{kaggle:dataset, | |
title = {Telugu News - Natural Language Processing for Indian Languages}, | |
authors={Sudalai Rajkumar, Anusha Motamarri}, | |
year={2019} | |
} | |
""" | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
This dataset contains Telugu language news articles along with respective | |
topic labels (business, editorial, entertainment, nation, sport) extracted from | |
the daily Andhra Jyoti. This dataset could be used to build Classification and Language Models. | |
""" | |
_HOMEPAGE = "https://www.kaggle.com/sudalairajkumar/telugu-nlp" | |
_LICENSE = "Data files © Original Authors" | |
class TeluguNews(datasets.GeneratorBasedBuilder): | |
"""Telugu News Articles with Topics.""" | |
VERSION = datasets.Version("1.1.0") | |
def manual_download_instructions(self): | |
return """\ | |
You need to visit Kaggle @ https://www.kaggle.com/sudalairajkumar/telugu-nlp, | |
and manually download the `telugu_news` dataset. This will download a file called | |
`telugu_news.zip` to your laptop. Unzip the file and move the two CSV files | |
(train and test) files to <path/to/folder>. You can then use | |
`datasets.load_dataset("telugu_news", data_dir="<path/to/folder>")` to load the datset. | |
""" | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"sno": datasets.Value("int32"), | |
"date": datasets.Value("string"), | |
"heading": datasets.Value("string"), | |
"body": datasets.Value("string"), | |
"topic": datasets.features.ClassLabel( | |
names=["business", "editorial", "entertainment", "nation", "sports"], | |
), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) | |
if not os.path.exists(data_dir): | |
raise FileNotFoundError( | |
f"{data_dir} does not exist. Download instructions: {self.manual_download_instructions} " | |
) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "train_telugu_news.csv"), | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "test_telugu_news.csv"), | |
"split": "test", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""Yields examples.""" | |
with open(filepath, encoding="utf-8") as csv_file: | |
csv_reader = csv.reader(csv_file) | |
next(csv_reader, None) # skip the headers | |
for id_, row in enumerate(csv_reader): | |
sno, date, heading, body, topic = row | |
yield id_, { | |
"sno": sno, | |
"date": date, | |
"heading": heading, | |
"body": body, | |
"topic": topic, | |
} | |