Delete loading script

hausa_voa_topics.py

@@ -1,91 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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
-"""Hausa VOA News Topic Classification dataset."""
-
-
-import csv
-
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-A collection of news article headlines in Hausa from VOA Hausa.
-Each headline is labeled with one of the following classes: Nigeria,
-Africa, World, Health or Politics.
-
-The dataset was presented in the paper:
-Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and
-Distant Supervision for Multilingual Transformer Models: A Study on
-African Languages (EMNLP 2020).
-"""
-
-_CITATION = """\
-@inproceedings{hedderich-etal-2020-transfer,
-    title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages",
-    author = "Hedderich, Michael A.  and
-      Adelani, David  and
-      Zhu, Dawei  and
-      Alabi, Jesujoba  and
-      Markus, Udia  and
-      Klakow, Dietrich",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
-    year = "2020",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
-    doi = "10.18653/v1/2020.emnlp-main.204",
-}
-"""
-
-_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/hausa_newsclass/train_clean.tsv"
-_VALIDATION_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/hausa_newsclass/dev.tsv"
-_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/uds-lsv/transfer-distant-transformer-african/master/data/hausa_newsclass/test.tsv"
-
-
-class HausaVOATopics(datasets.GeneratorBasedBuilder):
-    """Hausa VOA News Topic Classification dataset."""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "news_title": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=["Africa", "Health", "Nigeria", "Politics", "World"]),
-                }
-            ),
-            homepage="https://github.com/uds-lsv/transfer-distant-transformer-african",
-            citation=_CITATION,
-            task_templates=[TextClassification(text_column="news_title", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
-        validation_path = dl_manager.download_and_extract(_VALIDATION_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.VALIDATION, gen_kwargs={"filepath": validation_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate Hausa VOA News Topic examples."""
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.DictReader(csv_file, delimiter="\t")
-            for id_, row in enumerate(csv_reader):
-                yield id_, {"news_title": row["news_title"], "label": row["label"]}