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",
                            ]
                        )
                    ),
                }
            ),
            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:
    #             # Check if the delimiter ("\t") is present in the row
    #             if "\t" in row:
    #                 token, label = row.split("\t")
    #                 current_tokens.append(token)
    #                 current_labels.append(label)
    #         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,
    #         }
            
    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:
                    token, label = row.split("\t")
                    current_tokens.append(token)
                    current_labels.append(label)
                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 last sentence in dataset 🧐
            if current_tokens:
                yield sentence_counter, {
                    "id": str(sentence_counter),
                    "tokens": current_tokens,
                    "ner_tags": current_labels,
                }