Class/Object

com.johnsnowlabs.nlp.annotators.pos.perceptron

PerceptronApproach

Related Docs: object PerceptronApproach | package perceptron

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class PerceptronApproach extends AnnotatorApproach[PerceptronModel] with PerceptronTrainingUtils

Averaged Perceptron model to tag words part-of-speech.

Sets a POS tag to each word within a sentence. Its train data (train_pos) is a spark dataset of POS format values with Annotation columns.

See https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs/nlp/annotators/pos/perceptron for further reference on how to use this API.

Linear Supertypes
PerceptronTrainingUtils, PerceptronUtils, AnnotatorApproach[PerceptronModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[PerceptronModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. PerceptronApproach
  2. PerceptronTrainingUtils
  3. PerceptronUtils
  4. AnnotatorApproach
  5. CanBeLazy
  6. DefaultParamsWritable
  7. MLWritable
  8. HasOutputAnnotatorType
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. Estimator
  12. PipelineStage
  13. Logging
  14. Params
  15. Serializable
  16. Serializable
  17. Identifiable
  18. AnyRef
  19. Any
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Visibility
  1. Public
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Instance Constructors

  1. new PerceptronApproach()

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  2. new PerceptronApproach(uid: String)

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    uid

    internal uid required to generate writable annotators

Type Members

  1. type AnnotatorType = String

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    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
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    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): PerceptronModel

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    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. val ambiguityThreshold: DoubleParam

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    How much percentage of total amount of words are covered to be marked as frequent

  7. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  8. def beforeTraining(spark: SparkSession): Unit

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    Definition Classes
    AnnotatorApproach
  9. def buildTagBook(taggedSentences: Array[TaggedSentence], frequencyThreshold: Int, ambiguityThreshold: Double): Map[String, String]

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    Finds very frequent tags on a word in training, and marks them as non ambiguous based on tune parameters ToDo: Move such parameters to configuration

    Finds very frequent tags on a word in training, and marks them as non ambiguous based on tune parameters ToDo: Move such parameters to configuration

    taggedSentences

    Takes entire tagged sentences to find frequent tags

    frequencyThreshold

    How many times at least a tag on a word to be marked as frequent

    ambiguityThreshold

    How much percentage of total amount of words are covered to be marked as frequent

    Definition Classes
    PerceptronTrainingUtils
  10. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  11. final def clear(param: Param[_]): PerceptronApproach.this.type

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    Definition Classes
    Params
  12. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  13. final def copy(extra: ParamMap): Estimator[PerceptronModel]

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    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  14. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
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    Params
  15. final def defaultCopy[T <: Params](extra: ParamMap): T

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  16. val description: String

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    veraged Perceptron model to tag words part-of-speech

    veraged Perceptron model to tag words part-of-speech

    Definition Classes
    PerceptronApproachAnnotatorApproach
  17. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  18. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  19. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  20. def explainParams(): String

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    Definition Classes
    Params
  21. final def extractParamMap(): ParamMap

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    Params
  22. final def extractParamMap(extra: ParamMap): ParamMap

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    Params
  23. def finalize(): Unit

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    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. final def fit(dataset: Dataset[_]): PerceptronModel

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    Definition Classes
    AnnotatorApproach → Estimator
  25. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[PerceptronModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  26. def fit(dataset: Dataset[_], paramMap: ParamMap): PerceptronModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  27. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): PerceptronModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  28. val frequencyThreshold: IntParam

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    How many times at least a tag on a word to be marked as frequent

  29. def generatesTagBook(dataset: Dataset[_]): Array[TaggedSentence]

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    Generates TagBook, which holds all the word to tags mapping that are not ambiguous

    Generates TagBook, which holds all the word to tags mapping that are not ambiguous

    Definition Classes
    PerceptronTrainingUtils
  30. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  31. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  32. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  33. def getInputCols: Array[String]

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    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  34. def getLazyAnnotator: Boolean

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    Definition Classes
    CanBeLazy
  35. def getNIterations: Int

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    Number of iterations for training.

    Number of iterations for training. May improve accuracy but takes longer. Default 5.

  36. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  37. final def getOutputCol: String

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    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  38. def getParam(paramName: String): Param[Any]

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    Params
  39. final def hasDefault[T](param: Param[T]): Boolean

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  40. def hasParam(paramName: String): Boolean

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    Params
  41. def hashCode(): Int

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    AnyRef → Any
  42. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
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    Logging
  43. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    protected
    Definition Classes
    Logging
  44. val inputAnnotatorTypes: Array[AnnotatorType]

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    Input annotator type: TOKEN, DOCUMENT

    Input annotator type: TOKEN, DOCUMENT

    Definition Classes
    PerceptronApproachHasInputAnnotationCols
  45. final val inputCols: StringArrayParam

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    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  46. final def isDefined(param: Param[_]): Boolean

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  47. final def isInstanceOf[T0]: Boolean

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    Any
  48. final def isSet(param: Param[_]): Boolean

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    Params
  49. def isTraceEnabled(): Boolean

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    Logging
  50. val lazyAnnotator: BooleanParam

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    Definition Classes
    CanBeLazy
  51. def log: Logger

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    Logging
  52. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  53. def logDebug(msg: ⇒ String): Unit

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    Logging
  54. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  55. def logError(msg: ⇒ String): Unit

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    Logging
  56. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  57. def logInfo(msg: ⇒ String): Unit

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    Logging
  58. def logName: String

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    Logging
  59. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  60. def logTrace(msg: ⇒ String): Unit

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    Logging
  61. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  62. def logWarning(msg: ⇒ String): Unit

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    Logging
  63. def msgHelper(schema: StructType): String

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    Attributes
    protected
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    HasInputAnnotationCols
  64. val nIterations: IntParam

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    Number of iterations in training, converges to better accuracy

  65. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  66. final def notify(): Unit

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    AnyRef
  67. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  68. def onTrained(model: PerceptronModel, spark: SparkSession): Unit

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    Definition Classes
    AnnotatorApproach
  69. val outputAnnotatorType: AnnotatorType

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    Output annotator type: POS

    Output annotator type: POS

    Definition Classes
    PerceptronApproachHasOutputAnnotatorType
  70. final val outputCol: Param[String]

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    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  71. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  72. val posCol: Param[String]

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    column of Array of POS tags that match tokens

  73. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  74. final def set(paramPair: ParamPair[_]): PerceptronApproach.this.type

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    Params
  75. final def set(param: String, value: Any): PerceptronApproach.this.type

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    Attributes
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    Params
  76. final def set[T](param: Param[T], value: T): PerceptronApproach.this.type

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    Definition Classes
    Params
  77. def setAmbiguityThreshold(value: Double): PerceptronApproach.this.type

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  78. final def setDefault(paramPairs: ParamPair[_]*): PerceptronApproach.this.type

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    Params
  79. final def setDefault[T](param: Param[T], value: T): PerceptronApproach.this.type

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  80. def setFrequencyThreshold(value: Int): PerceptronApproach.this.type

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  81. final def setInputCols(value: String*): PerceptronApproach.this.type

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    Definition Classes
    HasInputAnnotationCols
  82. final def setInputCols(value: Array[String]): PerceptronApproach.this.type

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    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  83. def setLazyAnnotator(value: Boolean): PerceptronApproach.this.type

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    Definition Classes
    CanBeLazy
  84. def setNIterations(value: Int): PerceptronApproach.this.type

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    Number of iterations for training.

    Number of iterations for training. May improve accuracy but takes longer. Default 5.

  85. final def setOutputCol(value: String): PerceptronApproach.this.type

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    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  86. def setPosColumn(value: String): PerceptronApproach.this.type

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    Column containing an array of POS Tags matching every token on the line.

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

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    Definition Classes
    AnyRef
  88. def toString(): String

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    Identifiable → AnyRef → Any
  89. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): PerceptronModel

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    Trains a model based on a provided CORPUS

    Trains a model based on a provided CORPUS

    returns

    A trained averaged model

    Definition Classes
    PerceptronApproachAnnotatorApproach
  90. def trainPerceptron(nIterations: Int, initialModel: TrainingPerceptronLegacy, taggedSentences: Array[TaggedSentence], taggedWordBook: Map[String, String]): AveragedPerceptron

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    Iterates for training

    Iterates for training

    Definition Classes
    PerceptronTrainingUtils
  91. final def transformSchema(schema: StructType): StructType

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    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    AnnotatorApproach → PipelineStage
  92. def transformSchema(schema: StructType, logging: Boolean): StructType

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    protected
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    PipelineStage
    Annotations
    @DeveloperApi()
  93. val uid: String

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    internal uid required to generate writable annotators

    internal uid required to generate writable annotators

    Definition Classes
    PerceptronApproach → Identifiable
  94. def validate(schema: StructType): Boolean

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    takes a Dataset and checks to see if all the required annotation types are present.

    takes a Dataset and checks to see if all the required annotation types are present.

    schema

    to be validated

    returns

    True if all the required types are present, else false

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    protected
    Definition Classes
    AnnotatorApproach
  95. final def wait(): Unit

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    @throws( ... )
  96. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  97. final def wait(arg0: Long): Unit

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    @throws( ... )
  98. def write: MLWriter

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    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from PerceptronTrainingUtils

Inherited from PerceptronUtils

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[PerceptronModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters