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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 calculateEmbeddingsDim(dataset: DataFrame): Int
6.  def clone(): AnyRef

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7.  def collectTrainingInstances(dataset: DataFrame, labelCol: String): Array[Array[(String, Array[Float])]]

   Converts DataFrame to Array of Arrays of Labels (string)

   Converts DataFrame to Array of Arrays of Labels (string)

   dataset

   Input DataFrame with embeddings and labels

   returns

   Array of Array of Map(String, Array(Float))

8.  def collectTrainingInstancesMultiLabel(dataset: DataFrame, labelCol: String): Array[Array[(Array[String], Array[Float])]]

   Converts DataFrame to labels and embeddings

   Converts DataFrame to labels and embeddings

   dataset

   Input DataFrame with embeddings and labels

   returns

   Array of Array of Map(Array(String), Array(Float))

9.  def decodeOutputData(tagIds: Array[Array[Float]]): Array[Array[(String, Float)]]

   Converts Tag Identifiers to Tag Names

   Converts Tag Identifiers to Tag Names

   tagIds

   Tag Ids encoded for Tensorflow Model.

   returns

   Tag names

10.  def encodeTags(labels: Array[String]): Array[Array[Int]]
11.  def encodeTagsMultiLabel(labels: Array[Array[String]]): Array[Array[Float]]
12.  final def eq(arg0: AnyRef): Boolean

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13.  def equals(arg0: Any): Boolean

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14.  def extractLabels(dataset: Array[Array[(String, Array[Float])]]): Array[String]

    Converts DataFrame to Array of Arrays of Labels (string)

    Converts DataFrame to Array of Arrays of Labels (string)

    dataset

    Input DataFrame with labels

    returns

    Array of Array of String

15.  def extractLabelsMultiLabel(dataset: Array[Array[(Array[String], Array[Float])]]): Array[Array[String]]

    Converts DataFrame to Array of Arrays of Labels (string)

    Converts DataFrame to Array of Arrays of Labels (string)

    dataset

    Input DataFrame with labels

    returns

    Array of Array of String

16.  def extractSentenceEmbeddings(docs: Seq[(Int, Seq[Annotation])]): Array[Array[Float]]

    Converts DataFrame to Array of Arrays of Embeddings

    Converts DataFrame to Array of Arrays of Embeddings

    docs

    Input DataFrame with sentence_embeddings

    returns

    Array of Array of Float

17.  def extractSentenceEmbeddings(dataset: Array[Array[(String, Array[Float])]]): Array[Array[Float]]

    Converts DataFrame to Array of Arrays of Embeddings

    Converts DataFrame to Array of Arrays of Embeddings

    dataset

    Input DataFrame with sentence_embeddings

    returns

    Array of Array of Float

18.  def extractSentenceEmbeddingsMultiLabel(docs: Seq[(Int, Seq[Annotation])]): Array[Array[Array[Float]]]

    Converts DataFrame to Array of arrays of arrays of arrays of Embeddings The difference in this function is to create a sequence in case of multiple sentences in a document Used in MultiClassifierDL

    Converts DataFrame to Array of arrays of arrays of arrays of Embeddings The difference in this function is to create a sequence in case of multiple sentences in a document Used in MultiClassifierDL

    docs

    Input DataFrame with sentence_embeddings

    returns

    Array of Arrays of Arrays of Floats

19.  def extractSentenceEmbeddingsMultiLabel(dataset: Array[Array[(Array[String], Array[Float])]]): Array[Array[Array[Float]]]

    Converts DataFrame to Array of arrays of arrays of arrays of Embeddings The difference in this function is to create a sequence in case of multiple sentences in a document Used in MultiClassifierDL

    Converts DataFrame to Array of arrays of arrays of arrays of Embeddings The difference in this function is to create a sequence in case of multiple sentences in a document Used in MultiClassifierDL

    dataset

    Input DataFrame with sentence_embeddings

    returns

    Array of Arrays of Arrays of Floats

20.  def extractSentenceEmbeddingsMultiLabelPredict(docs: Seq[(Int, Seq[Annotation])]): Array[Array[Array[Float]]]
21.  def finalize(): Unit

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22.  final def getClass(): Class[_]

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23.  def hashCode(): Int

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24.  final def isInstanceOf[T0]: Boolean

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

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26.  final def notify(): Unit

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27.  final def notifyAll(): Unit

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28.  val params: ClassifierDatasetEncoderParams
29.  final def synchronized[T0](arg0: ⇒ T0): T0

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30.  val tags: Array[String]
31.  val tags2Id: Map[String, Int]
32.  def toString(): String

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33.  final def wait(): Unit

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

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

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