Web Reference: Oct 13, 2025 · Encoder: The encoder takes the input data like a sentence and processes each word one by one then creates a single, fixed-size summary of the entire input called a context vector or latent space. Decoder: The decoder takes the context vector and begins to produce the output one step at a time. Sep 12, 2025 · While the original transformer paper introduced a full encoder-decoder model, variations of this architecture have emerged to serve different purposes. In this article, we will explore the different types of transformer models and their applications. Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.
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