This repository implements self-attention mechanisms as Keras layers for processing sequential data. It provides SeqSelfAttention layers that can be added to neural network architectures to consider context across all timestamps when computing relevance. The library integrates with standard Keras components like Embedding, LSTM, and Bidirectional layers, enabling attention-enhanced sequence models for tasks like text classification and sequence labeling.