object TransformerEncoder extends Serializable
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def
apply[S](numBlocks: Int, in: Int, attentionHiddenPerHeadDim: Int, attentionNumHeads: Int, mlpHiddenDim: Int, dropout: Double, padToken: Long, tOpt: STenOptions, linearized: Boolean)(implicit arg0: Sc[S]): TransformerEncoder
Factory for the encoder module of transformer Does *not* include embedding and positional encoding
Factory for the encoder module of transformer Does *not* include embedding and positional encoding
Input is
(data, tokens)
wheredata
is (batch, num tokens, in dimension), double tensortokens
is (batch,num tokens) long tensor.The sole purpose of
tokens
is to carry over the padding- numBlocks
number of transformer blocks to create
- in
input dimension
- attentionHiddenPerHeadDim
size of hidden attention dimension of each attention head
- attentionNumHeads
number of attention heads
- mlpHiddenDim
size of hidden dimension of the two layer perceptron
- dropout
dropout rate
- padToken
pad token, (batch, seq) positions where
tokens
==padToken
are ignored- tOpt
tensor options
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- implicit val load: Load[TransformerEncoder]
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toString(): String
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- implicit val trainingMode: TrainingMode[TransformerEncoder]
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