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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 biasesOpt: Option[MathColVector]
6.  def clone(): AnyRef

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   @throws(classOf[java.lang.CloneNotSupportedException]) @native()

7.  def concatenateModifierAndHead(sentenceHiddenStates: MathMatrix, modifierAbsolutePosition: Int, headRelativePosition: Int): MathMatrix

   Generates a 1-row matrix containing a concatenation of the modifier and head embeddings

8.  def concatenateModifiersAndHeads(sentenceHiddenStates: MathMatrix, headRelativePositions: Array[Int]): MathMatrix
9.  val dual: Boolean
10.  final def eq(arg0: AnyRef): Boolean

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11.  def equals(arg0: AnyRef): Boolean

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12.  def finalize(): Unit

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13.  def forward(inputBatch: Array[MathMatrix]): Array[MathMatrix]

    Forward pass for a batch of sentences

    Forward pass for a batch of sentences

    inputBatch

    Each matrix in the batch has dimensions (sentence size x hidden state size)

    returns

    Each output matrix has dimensions (sentence size x labels size)

14.  def forward(inputSentence: MathMatrix): MathMatrix

    Forward pass for a single sentence

15.  final def getClass(): Class[_ <: AnyRef]

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    @native()

16.  def hashCode(): Int

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    @native()

17.  final def isInstanceOf[T0]: Boolean

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18.  val labelsOpt: Option[Array[String]]
19.  val name: String
20.  final def ne(arg0: AnyRef): Boolean

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21.  final def notify(): Unit

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    @native()

22.  final def notifyAll(): Unit

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23.  def predict(inputBatch: Array[MathMatrix], batchHeads: Option[Array[Array[Int]]], batchMasks: Option[Array[Array[Boolean]]]): Array[Array[String]]

    Predict the top label for each token in each sentence in the batch

24.  def predict(inputSentence: MathMatrix, heads: Option[Array[Int]], masks: Option[Array[Boolean]]): Array[String]

    Predict the top label per token

25.  def predictDual(inputBatch: Array[MathMatrix], batchHeads: Option[Array[Array[Int]]] = None, batchMasks: Option[Array[Array[Boolean]]] = None): Array[Array[String]]

    Predict the top label for each combination of modifier token and corresponding head token

26.  def predictDualWithScores(inputBatch: Array[MathMatrix], batchHeads: Option[Array[Array[Array[Int]]]] = None, batchMasks: Option[Array[Array[Boolean]]] = None): Array[Array[Array[(String, Float)]]]
27.  def predictPrimal(inputBatch: Array[MathMatrix]): Array[Array[String]]
28.  def predictPrimalWithScores(inputBatch: Array[MathMatrix]): Array[Array[Array[(String, Float)]]]
29.  def predictWithScores(inputBatch: Array[MathMatrix], batchHeads: Option[Array[Array[Array[Int]]]], batchMasks: Option[Array[Array[Boolean]]]): Array[Array[Array[(String, Float)]]]

    Predict all labels and their scores per token in each sentence in the batch

30.  def predictWithScores(inputSentence: MathMatrix, heads: Option[Array[Array[Int]]], masks: Option[Array[Boolean]]): Array[Array[(String, Float)]]

    Predict all labels and their scores per token

31.  final def synchronized[T0](arg0: => T0): T0

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32.  def toString(): String

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33.  final def wait(): Unit

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    @throws(classOf[java.lang.InterruptedException])

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

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    @throws(classOf[java.lang.InterruptedException])

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

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    @throws(classOf[java.lang.InterruptedException]) @native()

36.  val weights: MathMatrix