class TensorflowT5 extends Serializable

This class is used to run T5 model for For Sequence Batches of WordpieceTokenizedSentence. Input for this model must be tokenized with a SentencePieceModel,

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Instance Constructors

  1. new TensorflowT5(tensorflow: TensorflowWrapper, spp: SentencePieceWrapper, configProtoBytes: Option[Array[Byte]] = None)

    tensorflow

    Albert Model wrapper with TensorFlowWrapper

    spp

    Albert SentencePiece model with SentencePieceWrapper

    configProtoBytes

    Configuration for TensorFlow session

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  5. def clone(): AnyRef
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    @throws( ... ) @native()
  6. def decode(sentences: Array[Array[Long]]): Seq[String]
  7. def encode(sentences: Seq[Annotation], task: String): Seq[Array[Long]]
  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: Any): Boolean
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  10. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  11. def generateSeq2Seq(sentences: Seq[Annotation], batchSize: Int = 1, maxOutputLength: Int, task: String): Seq[Annotation]
  12. final def getClass(): Class[_]
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  13. def hashCode(): Int
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  14. final def isInstanceOf[T0]: Boolean
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  15. final def ne(arg0: AnyRef): Boolean
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  16. final def notify(): Unit
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  17. final def notifyAll(): Unit
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  18. def process(batch: Seq[Array[Long]], maxOutputLength: Int = 200): Array[Array[Long]]
  19. val spp: SentencePieceWrapper
  20. final def synchronized[T0](arg0: ⇒ T0): T0
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  21. val tensorflow: TensorflowWrapper
  22. def toString(): String
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  23. final def wait(): Unit
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  24. final def wait(arg0: Long, arg1: Int): Unit
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  25. final def wait(arg0: Long): Unit
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