Datasets:
Tasks:
Text Classification
Languages:
Turkish
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
found
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. | |
# Lint as: python3 | |
"""TTC4900: A Benchmark Data for Turkish Text Categorization""" | |
import csv | |
import os | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_DESCRIPTION = """\ | |
The data set is taken from kemik group | |
http://www.kemik.yildiz.edu.tr/ | |
The data are pre-processed for the text categorization, collocations are found, character set is corrected, and so forth. | |
We named TTC4900 by mimicking the name convention of TTC 3600 dataset shared by the study http://journals.sagepub.com/doi/abs/10.1177/0165551515620551 | |
""" | |
_CITATION = "" | |
_LICENSE = "CC0: Public Domain" | |
_HOMEPAGE = "https://www.kaggle.com/savasy/ttc4900" | |
_FILENAME = "7allV03.csv" | |
class TTC4900Config(datasets.BuilderConfig): | |
"""BuilderConfig for TTC4900""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig for TTC4900. | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(TTC4900Config, self).__init__(**kwargs) | |
class TTC4900(datasets.GeneratorBasedBuilder): | |
"""TTC4900: A Benchmark Data for Turkish Text Categorization""" | |
BUILDER_CONFIGS = [ | |
TTC4900Config( | |
name="ttc4900", | |
version=datasets.Version("1.0.0"), | |
description="A Benchmark Data for Turkish Text Categorization", | |
), | |
] | |
def manual_download_instructions(self): | |
return """\ | |
You need to go to https://www.kaggle.com/savasy/ttc4900, | |
and manually download the ttc4900. Once it is completed, | |
a file named archive.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 7allV03.csv under <path/to/folder>. | |
The <path/to/folder> can e.g. be "~/manual_data". | |
ttc4900 can then be loaded using the following command `datasets.load_dataset("ttc4900", data_dir="<path/to/folder>")`. | |
""" | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"category": datasets.features.ClassLabel( | |
names=["siyaset", "dunya", "ekonomi", "kultur", "saglik", "spor", "teknoloji"] | |
), | |
"text": datasets.Value("string"), | |
} | |
), | |
supervised_keys=None, | |
# 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): | |
"""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('ttc4900', data_dir=...)` that includes a file name {}. Manual download instructions: {})".format( | |
path_to_manual_file, _FILENAME, self.manual_download_instructions | |
) | |
) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(path_to_manual_file, _FILENAME)} | |
) | |
] | |
def _generate_examples(self, filepath): | |
"""Generate TTC4900 examples.""" | |
logger.info("⏳ Generating examples from = %s", filepath) | |
with open(filepath, encoding="utf-8") as f: | |
rdr = csv.reader(f, delimiter=",") | |
next(rdr) | |
rownum = 0 | |
for row in rdr: | |
rownum += 1 | |
yield rownum, { | |
"category": row[0], | |
"text": row[1], | |
} | |