GlotSparse.py

# 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 [email protected] .
"""

_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"],
                            }