About Transformer
About Transformer
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The outputs of the self-consideration layer are fed to a feed-forward neural network. The exact same feed-forward network is independently applied to Each individual situation.
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Additional especially, to compute the next representation for just a offered term - “bank” one example is - the Transformer compares it to each other phrase inside the sentence. click here The results of these comparisons is definitely an awareness rating For each other phrase within the sentence. These consideration scores figure out exactly how much Just about every of one other text really should lead to the next illustration of “bank”.
Illustration: one particular-warm encoding of our output vocabulary Next this recap, let’s examine the design’s decline perform – the metric we are optimizing over the training stage to guide as many as a qualified and with any luck , incredibly accurate design.
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In the instance, the disambiguating “river” could receive a large attention rating when computing a different illustration for “lender”. The eye scores are then utilized as weights for just a weighted average of all text’ representations which can be fed into a fully-linked network to crank out a fresh representation for “bank”, reflecting that the sentence is discussing a river lender.
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