Transformer architecture explained?

The attention mechanism is the key innovation. It allows the model to focus on relevant parts of the input.

Self-attention enables parallel processing, which makes transformers much faster to train than RNNs.

The positional encoding is crucial for understanding sequence order since there's no inherent ordering.

Multi-head attention allows the model to attend to different types of relationships simultaneously.