UPLOAD_GUIDE.md

# Steel Material Classification Model Upload Guide

## Step 1: Get Hugging Face Token

1. Go to https://huggingface.co/settings/tokens
2. Click "New token"
3. Give it a name (e.g., "model-upload-token")
4. Select "Write" role
5. Copy the token

## Step 2: Login to Hugging Face

```bash
huggingface-cli login
# Enter your token when prompted
```

## Step 3: Create Model Repository

```bash
huggingface-cli repo create steel-material-classifier --type model
```

## Step 4: Upload Model

```bash
# Clone the repository
git clone https://huggingface.co/YOUR_USERNAME/steel-material-classifier
cd steel-material-classifier

# Copy all files from model_v24 directory
# Then commit and push
git add .
git commit -m "Initial commit: Steel material classification model"
git push
```

## Alternative: Direct Upload

```bash
# From the model_v24 directory
huggingface-cli upload YOUR_USERNAME/steel-material-classifier . --include "*.json,*.safetensors,*.pkl,*.md,*.txt,*.py"
```

## Files to Upload

### Required Files:
- ✅ config.json
- ✅ model.safetensors
- ✅ tokenizer.json
- ✅ tokenizer_config.json
- ✅ special_tokens_map.json
- ✅ label_mapping.json

### Optional Files:
- ✅ classifier.pkl
- ✅ label_embeddings.pkl
- ✅ label_embeddings.pkl.backup

### Documentation Files:
- ✅ README.md
- ✅ requirements.txt
- ✅ inference.py
- ✅ preprocessor.py
- ✅ model_card.md
- ✅ usage.md

## Model Information

- **Model Name**: steel-material-classifier
- **Base Model**: XLM-RoBERTa
- **Task**: Sequence Classification
- **Labels**: 66 steel industry materials
- **Languages**: Korean, English
- **Model Size**: ~1GB

## Usage After Upload

```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})")
```