Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Hindi
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
| import os | |
| import datasets | |
| from typing import List | |
| import json | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """ | |
| """ | |
| _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(SDUtestConfig, self).__init__(**kwargs) | |
| class HiNERCollapsedConfig(datasets.GeneratorBasedBuilder): | |
| """SDU Filtered 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="", | |
| citation=_CITATION, | |
| ) | |
| _URL = "https://huggingface.co/datasets/cfiltnlp/HiNER-collapsed/raw/main/" | |
| _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["dev"]}), | |
| 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'], | |
| } |