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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 bpeTokenizer: BioGPTTokenizer
6.  def clone(): AnyRef

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   protected[lang]

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   @throws( ... ) @native()

7.  def decode(sentences: Array[Array[Int]]): Seq[String]
8.  def encode(prompts: Seq[Annotation], isCaseSensitive: Boolean): Seq[Array[Int]]

   Attributes

   protected

9.  def encodeQA(questions: Seq[Annotation], contexts: Seq[Annotation], questionPrompt: String, isCaseSensitive: Boolean): Seq[Array[Int]]

   Attributes

   protected

10.  final def eq(arg0: AnyRef): Boolean

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11.  def equals(arg0: Any): Boolean

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12.  def finalize(): Unit

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    protected[lang]

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

13.  def generate(prompts: Seq[Annotation], batchSize: Int, maxNewTokens: Int, maxContextLength: Int, doSample: Boolean, topK: Int, randomSeed: Option[Int] = None, ignoreTokenIds: Array[Int] = Array(), isCaseSensitive: Boolean, stopAtEos: Boolean, noRepeatNgramSize: Int): Seq[Annotation]

    Definition Classes

    TensorflowBioGPT → MedicalEncoderDecoderModel

14.  def generateNoBeamSearch(inputIds: Seq[Array[Int]], maxContextLength: Int, maxNewTokens: Int, doSample: Boolean, topK: Int, vocabSize: Int, randomSeed: Option[Int], session: Session, ignoreTokenIds: Array[Int], questionAnswerTerminals: Array[Int], skipLastToken: Boolean, useCache: Boolean, returnContext: Boolean, stopAtEos: Boolean, noRepeatNgramSize: Int = 2): (Array[Array[Int]], Array[Float])
15.  final def getClass(): Class[_]

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    @native()

16.  def getGeneratedNgrams(prevInputIds: Seq[Array[Int]], generatedNgrams: Array[Map[IndexedSeq[Int], List[Int]]], hypoIdx: Int, curLen: Int, noRepeatNgramSize: Int): Array[Int]
17.  def hashCode(): Int

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    @native()

18.  final def isInstanceOf[T0]: Boolean

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

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20.  final def notify(): Unit

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    @native()

21.  final def notifyAll(): Unit

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    @native()

22.  def predict(sentences: Seq[Annotation], task: String, batchSize: Int, maxNewTokens: Int, maxTextLength: Int, doSample: Boolean, topK: Int, randomSeed: Option[Int] = None, ignoreTokenIds: Array[Int] = Array(), isCaseSensitive: Boolean, stopAtEos: Boolean, noRepeatNgramSize: Int, refineSummary: Boolean = false, refineSummaryTargetLength: Int = 100, refineChunkSize: Int = 512, refineMaxAttempts: Int = 3): Seq[Annotation]

    Definition Classes

    MedicalEncoderDecoderModel

23.  def predictQuestions(questionAndContexts: Seq[(Annotation, Annotation)], batchSize: Int, maxNewTokens: Int, maxContextLength: Int, doSample: Boolean, topK: Int, questionPrompt: String, randomSeed: Option[Int] = None, ignoreTokenIds: Array[Int] = Array(), isCaseSensitive: Boolean, questionAnswerTerminals: Array[Int], skipLastToken: Boolean, useCache: Boolean, noRepeatNgramSize: Int): Seq[Annotation]

    Definition Classes

    TensorflowBioGPT → MedicalEncoderDecoderModel

24.  def sessionWarmup(useCache: Boolean): Unit

    Attributes

    protected

25.  def sessionWarmup(): Unit

    Attributes

    protected

    Definition Classes

    TensorflowBioGPT → MedicalEncoderDecoderModel

26.  def softmax(scores: Array[Float]): Array[Float]
27.  final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes

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28.  def tag(batch: Seq[Array[Int]], maxNewTokens: Int, maxContextLength: Int, doSample: Boolean, topK: Int, randomSeed: Option[Int], ignoreTokenIds: Array[Int], questionAnswerTerminals: Array[Int], skipLastToken: Boolean, useCache: Boolean, returnContext: Boolean, stopAtEos: Boolean, noRepeatNgramSize: Int): (Array[Array[Int]], Array[Float])
29.  val tensorflow: TensorflowWrapper
30.  def toString(): String

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31.  final def wait(): Unit

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

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    @throws( ... )

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

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    @throws( ... ) @native()