ner.py

import datasets

logger = datasets.logging.get_logger(__name__)

_URL = "https://raw.githubusercontent.com/Kriyansparsana/demorepo/main/"
_TRAINING_FILE = "Indian_dataset_wnut_train.conll"
# _DEV_FILE = "indian_dataset.conll"
_TEST_FILE = "emerging.test.annotated"

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-creative-work",
                                "I-creative-work",
                                "B-group",
                                "I-group",
                                "B-location",
                                "I-location",
                                "B-person",
                                "I-person",
                                "B-product",
                                "I-product",          
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            # "dev": f"{_URL}{_DEV_FILE}",
            "test": f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

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