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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.  def calcStat(tp: Int, fp: Int, fn: Int): (Float, Float, Float)
6.  def clone(): AnyRef

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7.  val encoder: MedicalNerDatasetEncoder
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.  final def getClass(): Class[_]

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12.  def getInputDims: (Integer, Integer, Integer)
13.  def getLogName: String

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    TensorflowMedicalNer → Logging

14.  def getPiecesTags(tokenTags: TextSentenceLabels, sentence: WordpieceEmbeddingsSentence): Array[String]
15.  def getPiecesTags(tokenTags: Array[TextSentenceLabels], sentences: Array[WordpieceEmbeddingsSentence]): Array[Array[String]]
16.  def hashCode(): Int

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17.  final def isInstanceOf[T0]: Boolean

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18.  def log(value: ⇒ String, minLevel: Level): Unit

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    Logging

19.  val logger: Logger

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    Logging

20.  def measure(labeled: Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]], extended: Boolean = false, includeConfidence: Boolean = false, includeAllConfidenceScores: Boolean = false, enableOutputLogs: Boolean = false, outputLogsPath: String, batchSize: Int, description: String = "", uuid: String = Identifiable.randomUID("annotator")): (f1Score, macroF1Score)
21.  final def ne(arg0: AnyRef): Boolean

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22.  final def notify(): Unit

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23.  final def notifyAll(): Unit

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24.  def outputLog(value: ⇒ String, uuid: String, shouldLog: Boolean, outputLogsPath: String): Unit

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25.  def padBatch(batchInput: NerBatch): NerBatch
26.  def padTags(batchTags: Array[Array[Int]]): Array[Array[Int]]
27.  def predict(dataset: Array[WordpieceEmbeddingsSentence], configProtoBytes: Option[Array[Byte]] = None, includeConfidence: Boolean = false, includeAllConfidenceScores: Boolean = false, batchSize: Int = 1): Array[Array[(String, Option[Array[Map[String, String]]])]]
28.  def predictWithLoss(dataset: Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)], configProtoBytes: Option[Array[Byte]] = None, includeConfidence: Boolean = false, includeAllConfidenceScores: Boolean = false, batchSize: Int = 1): (Float, Array[Array[(String, Option[Array[Map[String, String]]])]])

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29.  def saveBestModel(): Session
30.  final def synchronized[T0](arg0: ⇒ T0): T0

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31.  def tagsForTokens(labels: Array[Array[(String, Option[Array[Map[String, String]]])]], pieces: Array[WordpieceEmbeddingsSentence]): Array[Array[(String, Option[Array[Map[String, String]]])]]
32.  def tagsForTokens(labels: Array[(String, Option[Array[Map[String, String]]])], pieces: WordpieceEmbeddingsSentence): Array[(String, Option[Array[Map[String, String]]])]
33.  val tensorflow: TensorflowWrapper
34.  def toString(): String

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35.  def train(dataSetGenerator: (Long) ⇒ (Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]], Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]]), trainLength: Long, validLength: Long, lr: Float, po: Float, dropout: Float, startEpoch: Int = 0, isPretrained: Boolean = false, endEpoch: Int, graphFileName: String = "", test: ⇒ Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]] = Iterator.empty, configProtoBytes: Option[Array[Byte]] = None, validationSplit: Float = 0.0f, evaluationLogExtended: Boolean = false, includeConfidence: Boolean = false, includeAllConfidenceScores: Boolean = false, enableOutputLogs: Boolean = false, outputLogsPath: String, uuid: String = Identifiable.randomUID("annotator"), batchSize: Int, useBestModel: Boolean = false, earlyStopping: Option[EarlyStopping] = None, randomValidationSplitPerEpoch: Boolean = false, randomSeed: Int): Session
36.  val verboseLevel: nlp.annotators.ner.Verbose.Value

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    TensorflowMedicalNer → Logging

37.  final def wait(): Unit

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

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

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