Order the steps of how the Transformer architecture processes input data from initial encoding to final output generation.

The Transformer first turns each word into a vector with an embedding layer and adds a positional encoding so the model knows word order. These vectors are fed through several encoder layers that apply self‑attention to mix information from all positions, then a feed‑forward network, and residual connections. The encoder’s final output is a set of contextualized vectors that summarize the entire input. The decoder receives a start token and, step by step, applies self‑attention to its own generated tokens, attends to the encoder output, and then passes the result through a feed‑forward network. Finally a linear layer followed by softmax produces the probability of the next word, which is repeated until an end token is produced.