Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Hindi
Size:
100K<n<1M
ArXiv:
License:
File size: 3,141 Bytes
f0480f1 59b8561 f0480f1 |
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 |
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/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["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'],
} |