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Transformer Encoder part:
Here on the image you can see what each Transformer encoder part itself consists from three parts:
Self - Attention layer Feed Forward layer Add normalize
Regarding the feed-forward layer, we already know how this works. The main mystery here is the self - Attention layer. In order to understand self -attention layer better I will divide self-layer understanding into several parts where I will modify the usual seq2seq model encoder:
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Transformer Encoder part:
Standard Transformer Encoding Procedure
Role of Positional Embeddings in Order-Insensitive Models
Key Hyperparameters of a Transformer Encoder
Transformer Encoding of a Masked Bilingual Sentence Pair
Prefix Tuning
In a sequence-to-sequence model, the input is processed by a stack of six encoder layers that have identical structures. A proposal is made to modify this architecture so that all six encoder layers share the exact same set of weights, with the goal of reducing the total number of model parameters. Which statement best analyzes the primary consequence of this change on the model's ability to process information?
A sentence is fed into the encoder side of a Transformer model. Arrange the following steps in the correct sequence to describe how the initial input is processed by the stack of encoders.
Improving a Transformer's Contextual Understanding