Breaking Down Transformers: How Encoder-Decoder Attention Works
| Source: Dev.to | Original article
Transformers' encoder-decoder attention is explained. Weights are saved for the decoder.
As we reported on April 26 in "Understanding Transformers Part 13: Introducing Encoder–Decoder Attention", the concept of encoder-decoder attention is crucial in transformer models. Now, the latest installment, "Understanding Transformers Part 14: Calculating Encoder–Decoder Attention", delves deeper into the calculations behind this mechanism. This follow-up article aims to provide a clearer understanding of how encoder-decoder attention is computed, a vital component in sequence-to-sequence models.
The calculation of encoder-decoder attention is essential for the decoder to generate output sequences based on the input sequences processed by the encoder. This process involves using the query values from the decoder and the key and value vectors from the encoder to compute attention weights. The ability to accurately calculate these weights is critical for the model's performance, as it enables the decoder to focus on relevant parts of the input sequence when generating output.
As researchers and developers continue to explore and implement transformer models, a deeper understanding of encoder-decoder attention calculations will be vital. With the increasing adoption of transformer-based architectures in natural language processing and other applications, the insights gained from this article will be valuable for those looking to improve model performance and efficiency.
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