# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ille255/spotsync-ner", dtype="auto")Quick Links
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
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ille255/spotsync-ner")