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

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.

BLT architecture (source: arXiv) The encoder and decoder are lightweight models. The encoder takes in raw input bytes and creates the patch representations that are fed to the global transformer.

The transformer architecture consists of an encoder and a decoder. The encoder processes the input sequence, while the decoder generates the output sequence.

Encoder-Decoder topology for slot tagging This is an equally well-known Long Short-Term Memory topology for performing sequence-to-sequence classification.