Some technical details about how it works: The core architecture combines a transformer model for embeddings with a prototype memory system and an adaptive neu

Some technical details about how it works: The core architecture combines a transformer model for embeddings with a prototype memory system and an adaptive neural head. When adding new classes, it uses Elastic Weight Consolidation (EWC) to preserve performance on existing classes while learning new ones. This prevents the common problem of catastrophic forgetting. The prototype memory system maintains class prototypes that get updated efficiently as new examples are added, making it memory-efficient even with large datasets. All state (prototypes, examples, neural weights) can be saved and loaded, making it easy to deploy and update models in production. The library is built on PyTorch and integrates with the HuggingFace ecosystem. It's tested with Python 3.8+ and requires minimal dependencies. Let me know if you'd like me to explain any part in more detail!