CodeSwitching-TE-EN.py

# Loading script for the Telugu-English Codeswitch Transliterate dataset
import datasets

logger = datasets.logging.get_logger(__name__)

_CITATION = """ """

_DESCRIPTION = """Telugu English POS Codeswitch dataset.
               """

_HOMEPAGE = """https://zenodo.org/record/4762031"""

_URL = "https://huggingface.co/datasets/anishka/CodeSwitching-TE-EN/resolve/main/"
_TRAINING_FILE = "TE_EN.conllu"


class TeEnCodeSwitchConfig(datasets.BuilderConfig):
    """ Builder config for the Ancora Ca NER dataset """

    def __init__(self, **kwargs):
        """BuilderConfig for TeEnCodeSwitch.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(TeEnCodeSwitchConfig, self).__init__(**kwargs)


class TeEnCodeSwitch(datasets.GeneratorBasedBuilder):
    """ Te-En-CodeSwitch dataset."""

    BUILDER_CONFIGS = [
        TeEnCodeSwitchConfig(
            name="Te-En-CodeSwitch",
            version=datasets.Version("0.0.1"),
            description="Te-En-CodeSwitch dataset"
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "B-LOC",
                                "B-MISC",
                                "B-ORG",
                                "B-PER",
                                "I-LOC",
                                "I-MISC",
                                "I-ORG",
                                "I-PER",
                                "O"
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)
        print ("Downloading files: ")
        print (urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # TeEnCodeSwitch tokens are space separated
                    splits = line.split('\t')
                    tokens.append(splits[0])
                    ner_tags.append(splits[1].rstrip())
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }