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.  val beamSize: IntParam

   Beam size for the beam search algorithm (Default: 4)

6.  def clone(): AnyRef

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7.  val doSample: BooleanParam

   Whether or not to use sampling, use greedy decoding otherwise (Default: false)

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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11.  def getBeamSize: Int

12.  final def getClass(): Class[_]

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13.  def getDoSample: Boolean

14.  def getMaxOutputLength: Int

15.  def getMinOutputLength: Int

16.  def getNReturnSequences: Int

17.  def getNoRepeatNgramSize: Int

18.  def getRandomSeed: Option[Long]

19.  def getRepetitionPenalty: Double

20.  def getTask: Option[String]

21.  def getTemperature: Double

22.  def getTopK: Int

23.  def getTopP: Double

24.  def hashCode(): Int

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25.  final def isInstanceOf[T0]: Boolean

    Definition Classes

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26.  val maxInputLength: IntParam

    max length of the input sequence (Default: 0)

27.  val maxOutputLength: IntParam

    Maximum length of the sequence to be generated (Default: 20)

28.  val minOutputLength: IntParam

    Minimum length of the sequence to be generated (Default: 0)

29.  val nReturnSequences: IntParam

    The number of sequences to return from the beam search.

30.  final def ne(arg0: AnyRef): Boolean

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31.  val noRepeatNgramSize: IntParam

    If set to int > 0, all ngrams of that size can only occur once (Default: 0)

32.  final def notify(): Unit

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33.  final def notifyAll(): Unit

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34.  val randomSeed: Option[Long]

    Optional Random seed for the model.

    Optional Random seed for the model. Needs to be of type Int.

35.  val repetitionPenalty: DoubleParam

    The parameter for repetition penalty (Default: 1.0).

    The parameter for repetition penalty (Default: 1.0). 1.0 means no penalty. See this paper for more details.

36.  def setBeamSize(beamNum: Int): HasGeneratorProperties.this

37.  def setDoSample(value: Boolean): HasGeneratorProperties.this

38.  def setMaxInputLength(value: Int): HasGeneratorProperties.this
39.  def setMaxOutputLength(value: Int): HasGeneratorProperties.this

40.  def setMinOutputLength(value: Int): HasGeneratorProperties.this

41.  def setNReturnSequences(beamNum: Int): HasGeneratorProperties.this

42.  def setNoRepeatNgramSize(value: Int): HasGeneratorProperties.this

43.  def setRandomSeed(value: Long): HasGeneratorProperties.this

44.  def setRepetitionPenalty(value: Double): HasGeneratorProperties.this

45.  def setTask(value: String): HasGeneratorProperties.this

46.  def setTemperature(value: Double): HasGeneratorProperties.this

47.  def setTopK(value: Int): HasGeneratorProperties.this

48.  def setTopP(value: Double): HasGeneratorProperties.this

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

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50.  val task: Param[String]

    Set transformer task, e.g.

    Set transformer task, e.g. "summarize:" (Default: "").

51.  val temperature: DoubleParam

    The value used to module the next token probabilities (Default: 1.0)

52.  def toString(): String

    Definition Classes

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53.  val topK: IntParam

    The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)

54.  val topP: DoubleParam

    If set to float < 1.0, only the most probable tokens with probabilities that add up to topP or higher are kept for generation (Default: 1.0)

55.  final def wait(): Unit

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

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    @throws( ... )

57.  final def wait(arg0: Long): Unit

    Definition Classes

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    @throws( ... ) @native()