usage.md

# 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 고로에서 환원하여 선철을 제조하는 과정"

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, 석회석, 석유 코크스, 무연탄, 갈탄
- **Fuels**: 천연가스, 액화천연가스, 경유, 휘발유, 등유
- **Gases**: 일산화탄소, 메탄, 에탄, 고로가스, 코크스 오븐 가스
- **Products**: 강철, 선철, 철, 열간성형철 (HBI), 고온 성형 환원철
- **By-products**: 고로 슬래그, 압연 스케일, 분진, 슬러지, 절삭칩
- **Others**: 전기, 냉각수, 윤활유, 포장재, 열유입

## 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 = "천연가스를 연료로 사용하여 고로를 가열"

# Returns: "천연가스" with confidence score

```

### Batch Prediction
```python

texts = [

    "철광석을 고로에서 환원하여 선철을 제조하는 과정",

    "석회석을 첨가하여 슬래그를 형성"

]

# Returns: ["철광석", "석회석"] 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]

```