Delete loading script

ttc4900.py

@@ -1,130 +0,0 @@
-# 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 datasets
-from datasets.tasks import TextClassification
-
-
-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
-
-If you use the dataset in a paper, please refer https://www.kaggle.com/savasy/ttc4900 as footnote and cite one of the papers as follows:
-
-- A Comparison of Different Approaches to Document Representation in Turkish Language, SDU Journal of Natural and Applied Science, Vol 22, Issue 2, 2018
-- A comparative analysis of text classification for Turkish language, Pamukkale University Journal of Engineering Science Volume 25 Issue 5, 2018
-- A Knowledge-poor Approach to Turkish Text Categorization with a Comparative Analysis, Proceedings of CICLING 2014, Springer LNCS, Nepal, 2014.
-"""
-
-_CITATION = """\
-@article{doi:10.5505/pajes.2018.15931,
-author = {Yıldırım, Savaş and Yıldız, Tuğba},
-title = {A comparative analysis of text classification for Turkish language},
-journal = {Pamukkale Univ Muh Bilim Derg},
-volume = {24},
-number = {5},
-pages = {879-886},
-year = {2018},
-doi = {10.5505/pajes.2018.15931},
-note ={doi: 10.5505/pajes.2018.15931},
-
-URL = {https://dx.doi.org/10.5505/pajes.2018.15931},
-eprint = {https://dx.doi.org/10.5505/pajes.2018.15931}
-}
-"""
-
-_LICENSE = "CC0: Public Domain"
-_HOMEPAGE = "https://www.kaggle.com/savasy/ttc4900"
-_DOWNLOAD_URL = "https://raw.githubusercontent.com/savasy/TurkishTextClassification/master"
-_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 _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,
-            task_templates=[TextClassification(text_column="text", label_column="category")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        urls_to_download = {
-            "train": _DOWNLOAD_URL + "/" + _FILENAME,
-        }
-        downloaded_files = dl_manager.download(urls_to_download)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
-        ]
-
-    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],
-                }