The transformer’s encoder doesn’t just send a final step of encoding to the decoder; it transmits all hidden states and encodings.

The trend will likely continue for the foreseeable future. The importance of self-attention in transformers Depending on the application, a transformer model follows an encoder-decoder architecture.

An Encoder-decoder architecture in machine learning efficiently translates one sequence data form to another.

The Transformer architecture is made up of two core components: an encoder and a decoder. The encoder contains layers that process input data, like text and images, iteratively layer by layer.