hinglish-dump.py

# hinglish-dump.py

## About: This is a dataset script for diwank/silicone-merged
## Docs: https://huggingface.co/docs/datasets/dataset_script.html


"""Raw merged dump of Hinglish (hi-EN) datasets."""


import csv
import os

import datasets

_DESCRIPTION = """\
Raw merged dump of Hinglish (hi-EN) datasets.
"""

_HOMEPAGE = "https://huggingface.co/datasets/diwank/hinglish-dump"
_LICENSE = "MIT"

_URLS = {
    "crowd_transliteration": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
        "crowd_transliteration/crowd_transliterations.hi-en.txt",
    ])),
    "fire2013": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
        "fire2013/HindiEnglish_FIRE2013_AnnotatedDev.txt",
        "fire2013/HindiEnglish_FIRE2013_Test_GT.txt",
    ])),
    "hindi_romanized_dump": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
        "hindi_romanized_dump/hi_rom.txt",
    ])),
    "hindi_xlit": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
        "hindi_xlit/HiEn_ann1_test.json",
        "hindi_xlit/HiEn_ann1_train.json",
        "hindi_xlit/HiEn_ann1_valid.json",
    ])),
    "hinge": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
        "hinge/eval_human.csv",
        "hinge/eval_human.pkl",
        "hinge/train_human.csv",
        "hinge/train_human.pkl",
        "hinge/train_synthetic.csv",
        "hinge/eval_synthetic.csv",
    ])),
    "hinglish_norm": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
        "hinglish_norm/hinglishNorm_trainSet.json",
    ])),
    "news2018": list(map(lambda x: f"{_HOMEPAGE}/resolve/main/data/{x}" , [
        "news2018/NEWS2018_M-EnHi_tst.xml",
        "news2018/NEWS2018_M-EnHi_trn.xml",
        "news2018/NEWS2018_M-EnHi_dev.xml",
    ])),
}

config_names = _URLS.keys()
version = datasets.Version("1.0.0")

class HinglishDumpDataset(datasets.GeneratorBasedBuilder):
    """Raw merged dump of Hinglish (hi-EN) datasets."""

    VERSION = version
    CONFIGS = config_names

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name=subset, version=version, description=f"Config for {subset}")
        for subset in config_names
    ]

    def _info(self):
                
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(),
            homepage=_HOMEPAGE,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive

        data_dir = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.ALL,
            ),
        ]