Attention

Understand Self-Attention in Transformer Models

Transformers can process relationships between tokens without stepping through a sequence one token at a time. The mechanism that makes this possible is self-attention: each token builds a weighted view of other tokens in the same context. The formula is compact, but using transformer models well becomes easier when you understand what the calculation is doing, why masking matters, and where the computational cost comes from. Start with token representations Before attention runs, each input token is represented by a vector. Let the matrix X contain those token representations. A transformer layer applies learned projections to produce three matrices: