Encoder-decoder models are also known as sequence-to-sequence models. They are a type of artificial neural network architecture that is commonly used for natural language processing (NLP) tasks that involve converting an input sequence of data into an output sequence of data. For example, encoder-decoder models can be used for machine translation, where the input sequence is a sentence in one language and the output sequence is a translation of that sentence into another language.
The specific name that is used for an encoder-decoder model depends on the specific architecture of the model. For example, a transformer model is a type of encoder-decoder model that uses a self-attention mechanism to process the input sequence.
See Also: Decoder-only models, Encoder-only models