Delete GlotSparse.py

GlotSparse.py

@@ -1,152 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The GlotSprase Authors.
-# Lint as: python3
-"""
-                                           GlotSprase
-"""
-
-""" This dataset loading script is built based on Hugging Face tutorial, OSCAR-2301's and CulturaX dataset script. """
-
-import os
-import collections
-
-import pandas as pd
-
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = """\
-    GlotSprase \
-"""
-
-_URL = "https://huggingface.co/datasets/kargaranamir/GlotSparse"
-
-_LICENSE = """
-We do not own any of the text from which these data has been extracted.
-We license the actual packaging, the metadata and the annotations of these data under the cc0-1.0 (waiving all of the rights under copyright law).
-
-If you are a website/dataset owner and do not want your data to be included in this corpra, please send us an email at amir@cis.lmu.de .
-"""
-
-_CITATION = r"""\
-@misc{GlotSparse,
-  author = {Kargaran, Amir Hossein},
-  title = {GlotSparse Corpus},
-  year = {2023},
-  publisher = {Github},
-  journal = {Github Repository},
-  howpublished = {{\\url{https://huggingface.co/datasets/kargaranamir/GlotSparse}}},
-}
-"""
-
-_BASE_DATA_PAT_FORMAT_STR = "{language}/{language}.csv"
-
-
-def _languages():
-    """Create the sorted dictionary of language codes, and language names.
-    Returns:
-      The sorted dictionary as an instance of `collections.OrderedDict`.
-    """
-    langs = {
-        "Balochi_Arab": "bal_Arab",
-        "Twi_Latn": "twi_Latn",
-        "Fanti_Latn": "fat_Latn",
-        "South-Azerbaijani_Arab": "azb_Arab",
-        "Southern-Kurdish_Arab": "sdh_Arab",
-        "Gurani_Arab": "hac_Arab",
-        "Brahui_Arab": "brh_Arab",
-        "Southern-Uzbek_Arab": "uzs_Arab",
-        "Kirmanjki_Latn": "kiu_Latn",
-        "Southern-Uzbek_Arab": "uzs_Arab",
-        "Gilaki_Arab": "glk_Arab",
-    }
-
-    langs = {v: k for k, v in langs.items()}
-    return collections.OrderedDict(sorted(langs.items()))
-
-
-class GlotConfig(datasets.BuilderConfig):
-    """GlotSprase corpus."""
-
-    def __init__(self, language: str, **kwargs):
-        """BuilderConfig for GlotSprase.
-        Args:
-            language (str): It has to contain 3-letter coded strings following the writing script with an underline in between. For example: "glk_Arab", "fat_Latn".
-            **kwargs: Keyword arguments forwarded to super.
-        """
-        # Validate the language.
-        if language not in _languages():
-            raise ValueError("Invalid language: %s " % language)
-
-        name = f"{language}"
-        description = (
-            f"Original {_languages()[language]} GlotSprase dataset from 2023"
-        )
-        super(GlotConfig, self).__init__(
-            name=name, description=description, **kwargs
-        )
-
-        # Additional attributes
-        self.language = language
-        self.base_data_path = _BASE_DATA_PAT_FORMAT_STR.format(language=language)
-
-
-class Glot(datasets.GeneratorBasedBuilder):
-    """GlotSprase"""
-
-    BUILDER_CONFIGS = [
-        GlotConfig(  # pylint: disable=g-complex-comprehension
-            language=language,
-            version=datasets.Version("1.0.0"),
-        )
-        for language in _languages()
-    ]
-    BUILDER_CONFIG_CLASS = GlotConfig
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "Source": datasets.Value("string"),
-                    "Content": datasets.Value("string"),
-                    "Length": datasets.Value("int64"),
-                    "Script": datasets.Value("string"),
-                    "ISO639-3": datasets.Value("string"),
-                    "Language": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_URL,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        data_urls = [self.config.base_data_path]
-        doc_files = dl_manager.download(
-            [url for url in data_urls if url.endswith(".csv")]
-        )
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"doc_files": doc_files}
-            ),
-        ]
-
-
-    def _generate_examples(self, doc_files):
-        """This function returns the data by iterating on all the files."""
-        for doc_i, doc_path in enumerate(doc_files):
-            df = pd.read_csv(doc_path)
-
-            for index, row in df.iterrows():
-                yield f"{doc_i}_{index}", {
-                            "ISO639-3": row["ISO639-3"],
-                            "Language": row["Language"],
-                            "Content": row["Content"],
-                            "Script": row["Script"],
-                            "Length": row["Length"],
-                            "Source": row["Source"],
-                            }