indian_names.py

import datasets


logger = datasets.logging.get_logger(__name__)


_URL = "https://raw.githubusercontent.com/Kriyansparsana/demorepo/main/wnut17train%20(1).conll"

class indian_namesConfig(datasets.BuilderConfig):
    """The WNUT 17 Emerging Entities Dataset."""

    def __init__(self, **kwargs):
        """BuilderConfig for WNUT 17.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(indian_namesConfig, self).__init__(**kwargs)


class indian_names(datasets.GeneratorBasedBuilder):
    """The WNUT 17 Emerging Entities Dataset."""

    BUILDER_CONFIGS = [
        indian_namesConfig(
            name="indian_names", version=datasets.Version("1.0.0"), description="The WNUT 17 Emerging Entities Dataset"
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "O",
                                "B-corporation",
                                "I-corporation",
                                "B-person",
                                "I-person"
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}",
        }
        downloaded_files = dl_manager.download_and_extract(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:
        current_tokens = []
        current_labels = []
        sentence_counter = 0
        for row in f:
            row = row.rstrip()
            if row:
                if "\t" in row:
                    token, label = row.split("\t")
                    current_tokens.append(token)
                    current_labels.append(label)
                else:
                    # Handle cases where the delimiter is missing
                    # You can choose to skip these rows or handle them differently
                    logger.warning(f"Delimiter missing in row: {row}")
            else:
                # New sentence
                if not current_tokens:
                    # Consecutive empty lines will cause empty sentences
                    continue
                assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
                sentence = (
                    sentence_counter,
                    {
                        "id": str(sentence_counter),
                        "tokens": current_tokens,
                        "ner_tags": current_labels,
                    },
                )
                sentence_counter += 1
                current_tokens = []
                current_labels = []
                yield sentence
        # Don't forget the last sentence in the dataset 🧐
        if current_tokens:
            yield sentence_counter, {
                "id": str(sentence_counter),
                "tokens": current_tokens,
                "ner_tags": current_labels,
            }