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I'm curious about the reason for making self-attention for each classifier layer. #37

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wonbeeny opened this issue Mar 24, 2021 · 0 comments

wonbeeny commented Mar 24, 2021

First of all, thanks for your kind offer.

What do you think is the reason for self-attention for each classifier layer?

The paper also says that it does self-attention in 128 dimensions.

What do you think is the difference from deriving a result without self-attention with a only hidden size of 768 dimensions?

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