ph_en_text_detoxed.py

# 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/blob/main/ph_en_text_detoxed_train.csv"
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/NLPinas/ph-en-text/blob/main/ph_en_text_detoxed_test.csv"


class PhEnText(datasets.GeneratorBasedBuilder):
    """Philippine English Text (PhEnText) Corpus."""

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("int"),
                "text": datasets.Value("string"),
            }
            )
            
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features
            homepage=" ",
            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):
                id, text = row
                if row.strip():                
                    yield id_, {"id": id, "text": text}
                else:
                    yield id_, {"id": id, "text": ""}