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:
File size: 4,771 Bytes
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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,
# }
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