| --- |
| language: |
| - ko |
| license: apache-2.0 |
| tags: |
| - ner |
| - token-classification |
| - korean |
| - place-search |
| - bert |
| library_name: transformers |
| pipeline_tag: token-classification |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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](https://huggingface.co/ille255/spotsync-ner-onnx) |
| - GitHub: [SpotSync Project](https://github.com/IlleJiViN/comp_team) |
|
|
