ille255/spotsync-ner

SpotSync NER - Korean Place Search NER Model

Korean Named Entity Recognition model used in the SpotSync intelligent place search pipeline. Extracts Location (LOC), Brand (BRAND), Category (CAT), and Attribute (ATTR) from natural language queries.

Labels

Label	Description	Example
B-LOC / I-LOC	Location/region	Hongdae, Gangnam, Sinchon Station
B-BRAND / I-BRAND	Brand/store name	Starbucks, McDonald's
B-CAT / I-CAT	Business category	Cafe, Restaurant, Gym
B-ATTR / I-ATTR	Attribute/characteristic	Quiet, Good atmosphere, 24 hours

Usage

from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

model_id = "ille255/spotsync-ner"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForTokenClassification.from_pretrained(model_id)

text = "Hongdae nearby cozy cafe"
tokens = text.split()
inputs = tokenizer(tokens, is_split_into_words=True, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

predictions = torch.argmax(outputs.logits, dim=2)[0]
id2label = model.config.id2label
for token, pred in zip(tokens, predictions[1:-1]):
    print(f"{token}: {id2label[pred.item()]}")

Model Architecture

• Base: BERT (BertForTokenClassification)
• Hidden Size: 768
• Attention Heads: 12
• Layers: 12
• Vocab Size: 32,000

Related Links

• ONNX Quantized version: ille255/spotsync-ner-onnx
• GitHub: SpotSync Project