The Transformer is a neural network architecture for machine translation that uses attention mechanisms to process input sequences. It consists of an encoder component (stacked encoders) and a decoder component (stacked decoders), with each encoder and decoder having self-attention and feed-forward neural network layers. This diagram illustrates the complete Transformer architecture, showing how the model processes input through encoder and decoder stacks, utilizing multi-head attention mechanisms and position encodings to understand and generate sequences. The architecture revolutionized natural language processing by enabling parallel processing and capturing long-range dependencies more effectively than previous sequential models.