: These convert discrete tokens (words or characters) into fixed-size vectors that capture initial semantic meaning.

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

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

The is a deep learning architecture that relies on parallelized attention mechanisms rather than sequential recurrence. Its primary components are organized into an Encoder and a Decoder , which work together to transform input sequences into contextualized representations and subsequently into output sequences. 1. Input Processing: Embedding & Positional Encoding

This consists of two linear transformations with a non-linear activation (typically ReLU) in between.