A position-wise feed-forward network is a crucial component of the transformer architecture that applies a series of linear transformations and nonlinear activations to each position independently within the input sequence. This means that every token in the input gets processed through the same network without taking into account its neighboring tokens, enabling the model to learn complex representations and features for each individual token while still being part of a larger sequence. This network enhances the expressiveness of the transformer by adding depth and allowing for more sophisticated modeling of relationships between tokens.
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