Some benchmarks showing the advantage over traditional approaches:

Traditional classifier adding a new class:

- Requires full retraining (~30-60 minutes on typical dataset)

- Needs all historical data

- Uses 2-3x more memory during training

This approach:

- Adds new class in seconds

- Needs only examples of new class

- Memory usage stays constant

- Maintains 95%+ accuracy on existing classes

The code is well-documented and tested. I've included detailed examples showing:

- Batch processing for large datasets

- Multi-language support

- Model persistence

- Custom transformer models

Happy to share more details about the architecture or specific implementation challenges!