# Benchmark Training Status

## Training Started

The full benchmark training has been started with the following configuration:

- **Training Data**: 5,440 texts
- **Validation Data**: 680 texts  
- **Test Data**: 680 texts
- **Epochs**: 5
- **Batch Size**: 4
- **Learning Rate**: 1e-3
- **Device**: GPU (NVIDIA GeForce RTX 5060)
- **Output Directory**: `./checkpoints/benchmark_run`

## Configuration

- Model uses formula-based attention (no memory buffer to avoid CUDA issues)
- Evaluation runs every 2 epochs
- Best model saved based on validation loss
- Final benchmark comparison report will be generated

## Monitor Progress

Check training progress:
```bash
python scripts/check_training.py ./checkpoints/benchmark_run
```

Or check GPU usage:
```bash
nvidia-smi
```

## Expected Output Files

After training completes, you'll find:
- `./checkpoints/benchmark_run/best_model.pt` - Best model checkpoint
- `./checkpoints/benchmark_run/eval_results_epoch_X.json` - Evaluation results per epoch
- `./checkpoints/benchmark_run/final_benchmark_results.json` - Final benchmark metrics
- Comparison report showing performance vs. baseline models

## Training Time Estimate

- **Per epoch**: ~10-30 minutes depending on GPU
- **Total (5 epochs)**: ~1-2 hours
- Includes evaluation and benchmark metrics

## If Training Fails

1. Check logs for errors
2. Reduce batch_size if GPU out of memory
3. Verify GPU is available: `python -c "import torch; print(torch.cuda.is_available())"`

Training is running - check progress using the commands above!