Hugging Face's logo

Datasets:
cfilt
/
HiNER-original

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:
Dataset card Viewer Files Community
1
HiNER-original
File size: 3,957 Bytes 
696ae39
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
51e6626
696ae39
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7af0927
696ae39
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
 
import os

import datasets
from typing import List
import json

logger = datasets.logging.get_logger(__name__)


_CITATION = """
"""

_DESCRIPTION = """
This is the dataset repository for HiNER Dataset accepted to be published at LREC 2022.
The dataset can help build sequence labelling models for the task Named Entity Recognitin for the Hindi language.
"""

class HiNERConfig(datasets.BuilderConfig):
    """BuilderConfig for HiNER Dataset."""

    def __init__(self, **kwargs):
        """BuilderConfig for HiNER.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(HiNERConfig, self).__init__(**kwargs)


class HiNERConfig(datasets.GeneratorBasedBuilder):
    """HiNER dataset."""

    BUILDER_CONFIGS = [
        HiNERConfig(name="HiNER", 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",
                                "B-FESTIVAL",
                                "I-FESTIVAL",
                                "B-GAME",
                                "I-GAME",
                                "B-LANGUAGE",
                                "I-LANGUAGE",
                                "B-LITERATURE",
                                "I-LITERATURE",
                                "B-MISC",
                                "I-MISC",
                                "B-NUMEX",
                                "I-NUMEX",
                                "B-RELIGION",
                                "I-RELIGION",
                                "B-TIMEX",
                                "I-TIMEX",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/cfiltnlp/HiNER",
            citation=_CITATION,
        )

    _URL = "https://huggingface.co/datasets/cfilt/HiNER-original/resolve/main/data/"
    _URLS = {
        "train": _URL + "train.json",
        "dev": _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:
            hiner = json.load(f)
            for object in hiner:
                id_ = int(object['id'])
                yield id_, {
                    "id": str(id_),
                    "tokens": object['tokens'],
                    # "pos_tags": object['pos_tags'],
                    "ner_tags": object['ner_tags'],
                }