Delete loading script

subscene.py

@@ -1,220 +0,0 @@
-#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.
-# TODO: Address all TODOs and remove all explanatory comments
-"""TODO: Add a description here."""
-
-
-import csv
-import json
-import os
-
-import datasets
-import gzip
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@misc{refine-ai,
-author = {Refine AI},
-title = {Subscene: A Large-Scale Multilingual Subtitle Dataset},
-year = {2024},
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-Subscene is a vast collection of multilingual subtitles, encompassing 65 different languages and consisting of more than 30 billion tokens with a total size of 410.70 GB. This dataset includes subtitles for movies, series, and animations gathered from the Subscene dump. It provides a rich resource for studying language variations and building multilingual NLP models. We have carefully applied a fastText classifier to remove any non-language content from incorrect subsets. Additionally, we performed basic cleaning and filtration. However, there is still room for further cleaning and refinement.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://archive.org/details/subscene-final-dump"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in _split_generators method)
-_URLS = "https://huggingface.co/datasets/refine-ai/subscene/resolve/main/{Lang}/{Lang}_subscene_{split}{index}.json.gz"
-
-
-_N_FILES_PER_SPLIT = {
-    # 'albanian': {'train': 1},
-    'arabic': {'train': 33},
-    'armenian': {'train': 1},
-    'azerbaijani': {'train': 1},
-    'basque': {'train': 1},
-    'belarusian': {'train': 1},
-    'bengali': {'train': 2},
-    'traditional_chinese': {'train': 1},
-    'bosnian': {'train': 1},
-    'brazillian_portuguese': {'train': 6},
-    'bulgarian': {'train': 1},
-    'bulgarian_english': {'train': 1},
-    'burmese': {'train': 1},
-    'cambodian_khmer': {'train': 1},
-    'catalan': {'train': 1},
-    'simplified_chinese': {'train': 1},
-    'croatian': {'train': 1},
-    'czech': {'train': 2},
-    'danish': {'train': 8},
-    'dutch': {'train': 4},
-    'dutch_english': {'train': 1},
-    'english': {'train': 82},
-    'english_german': {'train': 1},
-    'esperanto': {'train': 1},
-    'estonian': {'train': 1},
-    'farsi_persian': {'train': 23},
-    'finnish': {'train': 3},
-    'french': {'train': 12},
-    'georgian': {'train': 1},
-    'german': {'train': 2},
-    'greek': {'train': 3},
-    'greenlandic': {'train': 1},
-    'hebrew': {'train': 3},
-    'hindi': {'train': 1},
-    'hungarian': {'train': 1},
-    'hungarian_english': {'train': 1},
-    'icelandic': {'train': 1},
-    'indonesian': {'train': 25},
-    'italian': {'train': 7},
-    'japanese': {'train': 2},
-    'kannada': {'train': 1},
-    'korean': {'train': 3},
-    'kurdish': {'train': 1},
-    'latvian': {'train': 1},
-    'lithuanian': {'train': 1},
-    'macedonian': {'train': 1},
-    'malay': {'train': 4},
-    'malayalam': {'train': 1},
-    'manipuri': {'train': 1},
-    'mongolian': {'train': 1},
-    'nepali': {'train': 1},
-    'norwegian': {'train': 5},
-    'pashto': {'train': 1},
-    'polish': {'train': 2},
-    'portuguese': {'train': 3},
-    'punjabi': {'train': 1},
-    'romanian': {'train': 3},
-    'russian': {'train': 1},
-    'serbian': {'train': 1},
-    'sinhala': {'train': 1},
-    'slovak': {'train': 1},
-    'slovenian': {'train': 1},
-    'somali': {'train': 1},
-    'spanish': {'train': 7},
-    'sundanese': {'train': 1},
-    'swahili': {'train': 1},
-    'swedish': {'train': 5},
-    'tagalog': {'train': 1},
-    'tamil': {'train': 1},
-    'telugu': {'train': 1},
-    'thai': {'train': 4},
-    'turkish': {'train': 5},
-    'ukranian': {'train': 1},
-    'urdu': {'train': 1},
-    'vietnamese': {'train': 10},
-    'yoruba': {'train': 1}
-}
-
-_LangID = ['arabic', 'armenian', 'azerbaijani', 'basque', 'belarusian', 'bengali', 'traditional_chinese', 'bosnian', 'brazillian_portuguese', 'bulgarian', 'bulgarian_english', 'burmese', 'cambodian_khmer', 'catalan', 'simplified_chinese', 'croatian', 'czech', 'danish', 'dutch', 'dutch_english', 'english', 'english_german', 'esperanto', 'estonian', 'farsi_persian', 'finnish', 'french', 'georgian', 'german', 'greek', 'greenlandic', 'hebrew', 'hindi', 'hungarian', 'hungarian_english', 'icelandic', 'indonesian', 'italian', 'japanese', 'kannada', 'korean', 'kurdish', 'latvian', 'lithuanian', 'macedonian', 'malay', 'malayalam', 'manipuri', 'mongolian', 'nepali', 'norwegian', 'pashto', 'polish', 'portuguese', 'punjabi', 'romanian', 'russian', 'serbian', 'sinhala', 'slovak', 'slovenian', 'somali', 'spanish', 'sundanese', 'swahili', 'swedish', 'tagalog', 'tamil', 'telugu', 'thai', 'turkish', 'ukranian', 'urdu', 'vietnamese', 'yoruba']
-
-class SubsceneConfig(datasets.BuilderConfig):
-    """ Builder config for Subscene Dataset. """
-
-    def __init__(self, subset, **kwargs):
-        super(SubsceneConfig, self).__init__(**kwargs)
-
-        if subset !="all":
-
-            self.subset = [subset]
-        else:
-            self.subset = _LangID
-
-class Subscene(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-    VERSION = datasets.Version("1.1.0")
-    BUILDER_CONFIGS_CLASS = SubsceneConfig
-    BUILDER_CONFIGS = [
-        SubsceneConfig(name=subset,
-        subset=subset,
-        version=datasets.Version("1.1.0", ""),
-        description='')
-        for subset in _LangID
-    ]
-
-
-    def _info(self):
-        # information about the datasets and feature type of the datasets items.
-
-        features = datasets.Features(
-            {
-                "subtitle_name": datasets.Value("string"),
-                "file_name": datasets.Value("string"),
-                "transcript": datasets.Value("string"),
-            }
-        )
-
-
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        #split = 'train'
-        #print("Split")
-        data_urls = {}
-        for split in ['train']: #'validation']:
-            #if self.config.subset = "all":
-
-            data_urls[split] = [
-                _URLS.format(
-                    Lang = subset,
-                    split='validation' if split=='_val' else '',
-                    index = i,
-                )
-                for subset in self.config.subset
-                for i in range(_N_FILES_PER_SPLIT[subset][split])
-            ]
-
-        train_downloaded_files = dl_manager.download(data_urls["train"])
-        #validation_downloaded_files = dl_manager.download(data_urls["validation"])
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
-            #datasets.SplitGenerator(
-            #    name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
-            #),
-        ]
-
-
-    # method parameters are unpacked from gen_kwargs as given in _split_generators
-    def _generate_examples(self, filepaths):
-
-        id_ = 0
-        for filepath in filepaths:
-            with gzip.open(open(filepath,"rb"), "rt", encoding = "utf-8") as f:
-                for row in f:
-                    if row:
-
-                        data = json.loads(row)
-                        yield id_, data
-                        id_ +=1