Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Hindi
Size:
100K<n<1M
ArXiv:
License:
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 | |
print(guid) | |
splits = line.split("\t") | |
tokens.append(splits[0]) | |
# 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, | |
} | |