Class

com.johnsnowlabs.ml.tensorflow

TensorflowT5

Related Doc: package tensorflow

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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)

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    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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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

  6. def createNextTokenLogitsPenalties(inputIds: Seq[Array[Long]], logits: Array[Array[Float]], repetitionPenalty: Double): Array[Array[Float]]

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  7. def decode(sentences: Array[Array[Long]]): Seq[String]

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  8. def encode(sentences: Seq[Annotation], task: String): Seq[Array[Long]]

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  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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  12. def generateNoBeamSearch(inputIds: Seq[Array[Long]], decoderEncoderStateTensors: Tensor, encoderAttentionMaskTensors: Tensor, maxOutputLength: Int, minOutputLength: Int, doSample: Boolean, temperature: Double, topK: Int, topP: Double, repetitionPenalty: Double, noRepeatNgramSize: Int, batch_size: Int, vocab_size: Int, randomSeed: Option[Long], session: Session, ignoreTokenIds: Array[Int] = Array()): Array[Array[Long]]

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  13. def generateSeq2Seq(sentences: Seq[Annotation], batchSize: Int, minOutputLength: Int, maxOutputLength: Int, doSample: Boolean, temperature: Double, topK: Int, topP: Double, repetitionPenalty: Double, noRepeatNgramSize: Int, task: String, randomSeed: Option[Long] = None, ignoreTokenIds: Array[Int] = Array()): Seq[Annotation]

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  14. final def getClass(): Class[_]

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  15. def getGeneratedNgrams(prevInputIds: Seq[Array[Long]], generatedNgrams: Array[Map[IndexedSeq[Long], List[Long]]], hypoIdx: Int, curLen: Int, noRepeatNgramSize: Int): Array[Long]

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  16. def hashCode(): Int

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  17. final def isInstanceOf[T0]: Boolean

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  18. final def ne(arg0: AnyRef): Boolean

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def process(batch: Seq[Array[Long]], minOutputLength: Int, maxOutputLength: Int, doSample: Boolean, temperature: Double, topK: Int, topP: Double, repetitionPenalty: Double, noRepeatNgramSize: Int, randomSeed: Option[Long], ignoreTokenIds: Array[Int] = Array()): Array[Array[Long]]

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  22. val spp: SentencePieceWrapper

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    Albert SentencePiece model with SentencePieceWrapper

  23. final def synchronized[T0](arg0: ⇒ T0): T0

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  24. val tensorflow: TensorflowWrapper

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    Albert Model wrapper with TensorFlowWrapper

  25. def toString(): String

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  26. final def wait(): Unit

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  27. final def wait(arg0: Long, arg1: Int): Unit

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  28. final def wait(arg0: Long): Unit

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