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Figure 7
(a) The encoder and decoder blocks of the Transformer. The architecture employs a self-attention mechanism to efficiently process sequences in parallel. Multi-head attention and positional encodings provide the model with rich contextual information. (b) The attention mechanism. The sequence of X′, Y′, Z′ and F′ is generated based on the importance or attention to specific input tokens X, Y, Z and F. The attention weights are calculated using a compatibility function that measures the relevance of each input to the output being generated.

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ISSN: 2053-2733
Volume 80| Part 1| January 2024| Pages 1-17
https://doi.org/10.1107/S2053273323009300
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