MIGRATION_TO_DEBERTA.md

# Migration from Hierarchical BERT to DeBERTa-base

## Summary

Successfully migrated the codebase from using **BERT-base-uncased** to **DeBERTa-base** (microsoft/deberta-base).

## Changes Made

### 1. Configuration (`config.py`)
- **Changed model name**: `bert_model_name` from `"bert-base-uncased"` to `"microsoft/deberta-base"`
- **Updated documentation**: References to "Legal-BERT" updated to "Legal-DeBERTa"

### 2. Model Architecture (`model.py`)
- **Updated imports and docstrings**: Changed references from BERT to DeBERTa
- **Modified forward pass**: DeBERTa doesn't have `pooler_output` like BERT. Changed to use `last_hidden_state[:, 0, :]` (CLS token) instead
- **Updated both model classes**:
  - `FullyLearningBasedLegalBERT`: Now uses DeBERTa
  - `HierarchicalLegalBERT`: Now uses DeBERTa hierarchically
- **Fixed tokenizer**: Default model changed to `"microsoft/deberta-base"`
- **Dynamic hidden size**: Model now gets hidden size from config (still 768 for DeBERTa-base)

### 3. Training Scripts (`train.py`, `trainer.py`)
- Updated documentation and print statements to reference DeBERTa instead of BERT

## Key Technical Differences

### BERT vs DeBERTa
| Feature | BERT | DeBERTa |
|---------|------|---------|
| Model | `bert-base-uncased` | `microsoft/deberta-base` |
| Hidden Size | 768 | 768 |
| Pooler Output | ✅ Available | ❌ Not available |
| CLS Token | `outputs.pooler_output` | `outputs.last_hidden_state[:, 0, :]` |
| Attention | Standard | Disentangled attention |

### Why DeBERTa?
1. **Improved Performance**: DeBERTa uses disentangled attention mechanism
2. **Better Context Understanding**: Position-aware attention
3. **State-of-the-art**: Generally outperforms BERT on many benchmarks

## No Breaking Changes
- ✅ Model architecture remains the same (hierarchical structure intact)
- ✅ Training pipeline unchanged
- ✅ All multi-task heads (classification, severity, importance) work as before
- ✅ Loss functions and optimization unchanged
- ✅ Data loading and preprocessing unchanged

## Next Steps

### Before Training
1. Ensure transformers library is up to date:
   ```bash
   pip install --upgrade transformers
   ```

2. The first training run will download DeBERTa-base model (~360MB)

### Training
Simply run your existing training command:
```bash
python train.py --epochs 20 --batch-size 16
```

The model will automatically:
- Download DeBERTa-base from Hugging Face
- Use the hierarchical architecture with DeBERTa as encoder
- Save checkpoints with DeBERTa weights

### Model Compatibility
- Old BERT checkpoints will NOT be compatible with new DeBERTa model
- You'll need to retrain from scratch
- This is expected and necessary when changing the base encoder

## Files Modified
1. ✅ `config.py` - Model name and documentation
2. ✅ `model.py` - Model architecture and forward pass
3. ✅ `train.py` - Training script documentation
4. ✅ `trainer.py` - Trainer documentation

## Files NOT Modified (still work as-is)
- `data_loader.py` - No changes needed
- `evaluate.py` - Works with new model
- `inference.py` - Works with new model
- `risk_discovery.py` - Independent of encoder choice
- All other utility files

## Performance Expectations
DeBERTa should provide:
- Similar or better accuracy on risk classification
- Better handling of legal text nuances
- Potentially faster convergence during training