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# Steel Material Classification Model
## Quick Start
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model
model_name = "your-username/steel-material-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Predict
text = "철광석을 κ³ λ‘œμ—μ„œ ν™˜μ›ν•˜μ—¬ 선철을 μ œμ‘°ν•˜λŠ” κ³Όμ •"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(predictions, dim=1).item()
label = model.config.id2label[predicted_class]
confidence = predictions[0][predicted_class].item()
print(f"Predicted: {label} (Confidence: {confidence:.4f})")
```
## Model Information
- **Base Model**: XLM-RoBERTa
- **Task**: Sequence Classification
- **Labels**: 66 steel industry materials
- **Languages**: Korean, English
- **Model Size**: ~1GB
## Supported Labels
The model can classify 66 different steel industry materials including:
- **Raw Materials**: 철광석, μ„νšŒμ„, μ„μœ  μ½”ν¬μŠ€, 무연탄, κ°ˆνƒ„
- **Fuels**: μ²œμ—°κ°€μŠ€, μ•‘ν™”μ²œμ—°κ°€μŠ€, 경유, 휘발유, λ“±μœ 
- **Gases**: μΌμ‚°ν™”νƒ„μ†Œ, 메탄, 에탄, κ³ λ‘œκ°€μŠ€, μ½”ν¬μŠ€ 였븐 κ°€μŠ€
- **Products**: κ°•μ² , μ„ μ² , μ² , μ—΄κ°„μ„±ν˜•μ²  (HBI), 고온 μ„±ν˜• ν™˜μ›μ² 
- **By-products**: 고둜 슬래그, μ••μ—° μŠ€μΌ€μΌ, λΆ„μ§„, μŠ¬λŸ¬μ§€, μ ˆμ‚­μΉ©
- **Others**: μ „κΈ°, λƒ‰κ°μˆ˜, μœ€ν™œμœ , 포μž₯재, μ—΄μœ μž…
## Performance
- **Label Independence**: Good (average similarity: 0.1166)
- **Orthogonality**: Good (average dot product: 0.2043)
- **Overall Assessment**: The model shows good separation between different material categories
## Usage Examples
### Single Prediction
```python
text = "μ²œμ—°κ°€μŠ€λ₯Ό μ—°λ£Œλ‘œ μ‚¬μš©ν•˜μ—¬ 고둜λ₯Ό κ°€μ—΄"
# Returns: "μ²œμ—°κ°€μŠ€" with confidence score
```
### Batch Prediction
```python
texts = [
"철광석을 κ³ λ‘œμ—μ„œ ν™˜μ›ν•˜μ—¬ 선철을 μ œμ‘°ν•˜λŠ” κ³Όμ •",
"μ„νšŒμ„μ„ μ²¨κ°€ν•˜μ—¬ 슬래그λ₯Ό ν˜•μ„±"
]
# Returns: ["철광석", "μ„νšŒμ„"] with confidence scores
```
## Installation
```bash
pip install torch transformers
```
## License
[Add your license information]
## Citation
If you use this model in your research, please cite:
```bibtex
[Add citation information here]
```