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object BertEncoder extends Serializable

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  4. def apply[S](maxLength: Int, vocabularySize: Int, segmentVocabularySize: Int, numBlocks: Int, embeddingDim: Int, attentionHiddenPerHeadDim: Int, attentionNumHeads: Int, mlpHiddenDim: Int, dropout: Double, padToken: Long, tOpt: STenOptions, linearized: Boolean, positionEmbedding: Option[STen])(implicit arg0: Sc[S]): BertEncoder

    Factory for the encoder module of Bert

    Factory for the encoder module of Bert

    Input is (tokens, segments) where tokens and segments are both (batch,num tokens) long tensor.

    maxLength

    maximum num token length

    vocabularySize

    vocabulary size

    numBlocks

    number of transformer blocks to create

    embeddingDim

    input embedding 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, padding is not the same as masking

    tOpt

    tensor options

    positionEmbedding

    optional float tensor of size (sequence length, embedding dimension) if missing the absolute positional embeddings from Vaswani et al 2017 is used Following the Bert paper the position embeddings are summed

    returns

    a module

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  12. implicit val load: Load[BertEncoder]
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  18. implicit val trainingMode: TrainingMode[BertEncoder]
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  22. object PositionalEmbeddingWeight extends LeafTag

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