Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
File size: 2,838 Bytes
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import datasets
logger = datasets.logging.get_logger(__name__)
csv_file_path = "/home/p21-0144/Downloads/indian-name-org.csv"
class indian_namesConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(indian_namesConfig, self).__init__(**kwargs)
class indian_names(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
indian_namesConfig(
name="indian_names_dataset",
version=datasets.Version("1.0.0"),
description="Indian Names Dataset",
),
]
def _info(self):
return datasets.DatasetInfo(
description="Indian Names dataset",
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"B-PER",
"I-ORG",
]
)
),
}
),
)
def _split_generators(self, dl_manager):
urls_to_download = {
"train": f"file://{csv_file_path}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
]
def _generate_examples(self, filepath):
with open(filepath, encoding="utf-8") as f:
current_tokens = []
current_labels = []
sentence_counter = 0
for row in f:
row = row.rstrip()
if row:
token, label = row.split(",")
current_tokens.append(token)
current_labels.append(label)
else:
if not current_tokens:
continue
assert len(current_tokens) == len(current_labels), "Mismatch between tokens and labels"
sentence = (
sentence_counter,
{
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_labels,
},
)
sentence_counter += 1
current_tokens = []
current_labels = []
yield sentence
if current_tokens:
yield sentence_counter, {
"id": str(sentence_counter),
"tokens": current_tokens,
"ner_tags": current_labels,
} |