Delete loading script

gnad10.py

@@ -1,88 +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.
-"""Ten Thousand German News Articles Dataset"""
-
-
-import csv
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-This dataset is intended to advance topic classification for German texts. A classifier that is efffective in
-English may not be effective in German dataset because it has a higher inflection and longer compound words.
-The 10kGNAD dataset contains 10273 German news articles from an Austrian online newspaper categorized into
-9 categories. Article titles and text are concatenated together and authors are removed to avoid a keyword-like
-classification on authors that write frequently about one category. This dataset can be used as a benchmark
-for German topic classification.
-"""
-
-_HOMEPAGE = "https://tblock.github.io/10kGNAD/"
-
-_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0"
-
-_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/tblock/10kGNAD/master/train.csv"
-_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/tblock/10kGNAD/master/test.csv"
-
-
-class Gnad10(datasets.GeneratorBasedBuilder):
-    """10k German news articles for topic classification"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(
-                        names=[
-                            "Web",
-                            "Panorama",
-                            "International",
-                            "Wirtschaft",
-                            "Sport",
-                            "Inland",
-                            "Etat",
-                            "Wissenschaft",
-                            "Kultur",
-                        ]
-                    ),
-                }
-            ),
-            homepage="https://tblock.github.io/10kGNAD/",
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate German news articles examples."""
-
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(csv_file, delimiter=";", quotechar="'", quoting=csv.QUOTE_ALL)
-            for id_, row in enumerate(csv_reader):
-                label, text = row
-                yield id_, {"text": text, "label": label}