HiNER-collapsed.py

import os

import datasets
from typing import List
import json

logger = datasets.logging.get_logger(__name__)


_CITATION = """
XX
"""

_DESCRIPTION = """
This is the repository for HiNER - a large Hindi Named Entity Recognition dataset.
"""

class HiNERCollapsedConfig(datasets.BuilderConfig):
    """BuilderConfig for Conll2003"""

    def __init__(self, **kwargs):
        """BuilderConfig forConll2003.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(HiNERCollapsedConfig, self).__init__(**kwargs)


class HiNERCollapsedConfig(datasets.GeneratorBasedBuilder):
    """HiNER Collapsed dataset."""

    BUILDER_CONFIGS = [
        HiNERCollapsedConfig(name="HiNER-Collapsed", version=datasets.Version("0.0.2"), description="Hindi Named Entity Recognition 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=[
                                "O",
                                "B-PERSON",
                                "I-PERSON",
                                "B-LOCATION",
                                "I-LOCATION",
                                "B-ORGANIZATION",
                                "I-ORGANIZATION"  
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="YY",
            citation=_CITATION,
        )

    _URL = "https://huggingface.co/datasets/cfilt/HiNER-collapsed/resolve/main/data/"
    _URLS = {
        "train": _URL + "train.json",
        "validation": _URL + "validation.json",
        "test": _URL + "test.json"
    }

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        urls_to_download = self._URLS
        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["validation"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath) as f:
            data = json.load(f)
            for object in data:
                id_ = int(object['id'])
                yield id_, {
                    "id": str(id_),
                    "tokens": object['tokens'],
                    #"pos_tags": object['pos_tags'],
                    "ner_tags": object['ner_tags'],
                }
    # def _generate_examples(self, filepath):
    #     logger.info("⏳ Generating examples from = %s", filepath)
    #     with open(filepath, encoding="utf-8") as f:
    #         guid = 0
    #         tokens = []
    #         # pos_tags = []
    #         # chunk_tags = []
    #         ner_tags = []
    #         for line in f:
    #             if line.startswith("-DOCSTART-") or line == "" or line == "\n":
    #                 if tokens:
    #                     yield guid, {
    #                         "id": str(guid),
    #                         "tokens": tokens,
    #                         # "pos_tags": pos_tags,
    #                         # "chunk_tags": chunk_tags,
    #                         "ner_tags": ner_tags,
    #                     }
    #                     guid += 1
    #                     tokens = []
    #                     # pos_tags = []
    #                     # chunk_tags = []
    #                     ner_tags = []
    #             else:
    #                 # conll2003 tokens are space separated
    #                 splits = line.split("\t")
    #                 tokens.append(splits[0].strip())
    #                 # pos_tags.append(splits[1])
    #                 # chunk_tags.append(splits[2])
    #                 ner_tags.append(splits[1].rstrip())
    #         # last example
    #         yield guid, {
    #             "id": str(guid),
    #             "tokens": tokens,
    #             # "pos_tags": pos_tags,
    #             # "chunk_tags": chunk_tags,
    #             "ner_tags": ner_tags,
    #         }