Delete ph_en_text_detoxed.py

ph_en_text_detoxed.py

@@ -1,77 +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
-"""Philippine English Text Corpus."""
-#template from agNews and phNews, tweet_eval
-
-import os
-import datasets
-import csv
-
-
-_DESCRIPTION = """\
-PhEnText Detoxed is a large-scale and multi-domain lexical data written in Philippine English text.
-The news articles, religious articles and court decisions collated by the original researchers were filtered for toxicity and special characters were further preprocessed.
-"""
-
-_CITATION = """\
-
-}
-"""
-
-_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/NLPinas/ph_en_text_detoxed/blob/main/ph_en_text_detoxed_train.csv"
-_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/NLPinas/ph_en_text_detoxed/blob/main/ph_en_text_detoxed_test.csv"
-
-
-class PhEnText(datasets.GeneratorBasedBuilder):
-    """Philippine English Text (PhEnText) Corpus."""
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "text": datasets.Value("string"),
-            }
-            )
-            
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        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):
-        """PhEnText examples."""
-        with open(filepath, encoding="utf-8") as csv_file:
-        
-            csv_reader = csv.reader(
-                    csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
-                )
-            
-            for id_, row in enumerate(csv_reader):
-                text = row
-                if row:                
-                    yield id_, {"text": text}
-                else:
-                    yield id_, {"text": ""}