Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
File size: 2,784 Bytes
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import datasets
logger = datasets.logging.get_logger(__name__)
_URL = "https://github.com/Kriyansparsana/demorepo/blob/main/test.txt"
class indian_namesConfig(datasets.BuilderConfig):
"""BuilderConfig for Conll2003"""
def __init__(self, **kwargs):
"""BuilderConfig forConll2003.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(indian_namesConfig, self).__init__(**kwargs)
class indian_names(datasets.GeneratorBasedBuilder):
"""Conll2003 dataset."""
BUILDER_CONFIGS = [
indian_namesConfig(name="indian_names", version=datasets.Version("1.0.0"), description="indian_names dataset"),
]
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"B-PER",
"B-ORG",
]
)
),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
downloaded_file = dl_manager.download_and_extract(_URL)
data_files = {
"train": downloaded_file,
}
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
ner_tags = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
}
guid += 1
ner_tags = []
else:
# conll2003 tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
ner_tags.append(splits[3].rstrip())
# last example
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"ner_tags": ner_tags,
} |