Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Hindi
Size:
100K<n<1M
ArXiv:
License:
File size: 4,733 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/raw/main/data/"
_URLS = {
"train": _URL + "train_clean.conll",
"validation": _URL + "validation_clean.conll",
"test": _URL + "test_clean.conll"
}
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].strip())
# 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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