Transformers Components Apr 2026

In the final stage of the decoder, the output vectors are transformed into human-readable results.

: Projects the decoder's output into a much larger vector (the size of the model's vocabulary).

It captures complex patterns that the attention mechanism might miss by processing each token's representation independently. 4. Normalization and Residual Connections transformers components

: Calculates a "relevance score" between tokens, allowing the model to understand how much focus one word should have on another (e.g., relating "he" to "Tom").

: These convert discrete tokens (words or characters) into fixed-size vectors that capture initial semantic meaning. In the final stage of the decoder, the

: These add the original input of a layer to its output before normalization, providing a "direct path" for gradients to flow backward during training. 5. Linear and Softmax Layers

Since Transformers do not process data sequentially like RNNs, they must explicitly "learn" the order of words. : These add the original input of a

This is the "core" of the architecture, allowing the model to focus on different parts of the input sequence simultaneously.