class RelationExtractionModel extends GenericClassifierModel

Extracts and classifies instances of relations between named entities. For this, relation pairs need to be defined with setRelationPairs, to specify between which entities the extraction should be done.

For pretrained models please see the Models Hub for available models.

Example

Relation Extraction between body parts

Define pipeline stages to extract entities

val documenter = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val sentencer = new SentenceDetector()
  .setInputCols("document")
  .setOutputCol("sentences")

val tokenizer = new Tokenizer()
  .setInputCols("sentences")
  .setOutputCol("tokens")

val words_embedder = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols("sentences", "tokens")
  .setOutputCol("embeddings")

val pos_tagger = PerceptronModel.pretrained("pos_clinical", "en", "clinical/models")
  .setInputCols("sentences", "tokens")
  .setOutputCol("pos_tags")

val dependency_parser = DependencyParserModel.pretrained("dependency_conllu", "en")
  .setInputCols("sentences", "pos_tags", "tokens")
  .setOutputCol("dependencies")

val clinical_ner_tagger = MedicalNerModel.pretrained("jsl_ner_wip_greedy_clinical","en","clinical/models")
  .setInputCols("sentences", "tokens", "embeddings")
  .setOutputCol("ner_tags")

val ner_chunker = new NerConverter()
  .setInputCols("sentences", "tokens", "ner_tags")
  .setOutputCol("ner_chunks")

Define the relations that are to be extracted

val relationPairs = Array("direction-external_body_part_or_region",
                      "external_body_part_or_region-direction",
                      "direction-internal_organ_or_component",
                      "internal_organ_or_component-direction")

val re_model = RelationExtractionModel.pretrained("re_bodypart_directions", "en", "clinical/models")
  .setInputCols("embeddings", "pos_tags", "ner_chunks", "dependencies")
  .setOutputCol("relations")
  .setRelationPairs(relationPairs)
  .setMaxSyntacticDistance(4)
  .setPredictionThreshold(0.9f)

val pipeline = new Pipeline().setStages(Array(
  documenter,
  sentencer,
  tokenizer,
  words_embedder,
  pos_tagger,
  clinical_ner_tagger,
  ner_chunker,
  dependency_parser,
  re_model
))

val data = Seq("MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia").toDF("text")
val result = pipeline.fit(data).transform(data)

Show results

result.selectExpr("explode(relations) as relations")
 .select(
   "relations.metadata.chunk1",
   "relations.metadata.entity1",
   "relations.metadata.chunk2",
   "relations.metadata.entity2",
   "relations.result"
 )
 .where("result != 0")
 .show(truncate=false)
+------+---------+-------------+---------------------------+------+
|chunk1|entity1  |chunk2       |entity2                    |result|
+------+---------+-------------+---------------------------+------+
|upper |Direction|brain stem   |Internal_organ_or_component|1     |
|left  |Direction|cerebellum   |Internal_organ_or_component|1     |
|right |Direction|basil ganglia|Internal_organ_or_component|1     |
+------+---------+-------------+---------------------------+------+
See also

RelationExtractionApproach to train your own model.

RelationExtractionDLModel for BERT based extraction

Linear Supertypes
GenericClassifierModel, CheckLicense, HasSimpleAnnotate[GenericClassifierModel], WriteTensorflowModel, HasStorageRef, AnnotatorModel[GenericClassifierModel], CanBeLazy, RawAnnotator[GenericClassifierModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[GenericClassifierModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
PosologyREModel
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. RelationExtractionModel
  2. GenericClassifierModel
  3. CheckLicense
  4. HasSimpleAnnotate
  5. WriteTensorflowModel
  6. HasStorageRef
  7. AnnotatorModel
  8. CanBeLazy
  9. RawAnnotator
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. HasOutputAnnotatorType
  13. ParamsAndFeaturesWritable
  14. HasFeatures
  15. DefaultParamsWritable
  16. MLWritable
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new RelationExtractionModel()
  2. new RelationExtractionModel(uid: String)

    uid

    a unique identifier for the instantiated AnnotatorModel

Type Members

  1. type AnnotationContent = Seq[Row]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

    takes a document and annotations and produces new annotations of this annotator's annotation type

    takes a document and annotations and produces new annotations of this annotator's annotation type

    annotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    RelationExtractionModel → GenericClassifierModel → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Definition Classes
    RelationExtractionModel → GenericClassifierModel → AnnotatorModel
  14. def categorizeRel(relation: RelationInstance): (Long, Float)
    Attributes
    protected
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. def checkValidEnvironment(spark: Option[SparkContext]): Unit
    Definition Classes
    CheckLicense
  17. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  18. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkContext], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  19. final def clear(param: Param[_]): RelationExtractionModel.this.type
    Definition Classes
    Params
  20. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  21. def copy(extra: ParamMap): GenericClassifierModel
    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  24. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. def dfAnnotate: UserDefinedFunction
    Definition Classes
    HasSimpleAnnotate
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  29. def explainParams(): String
    Definition Classes
    Params
  30. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  31. def extraValidateMsg: String
    Attributes
    protected
    Definition Classes
    RawAnnotator
  32. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  33. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  34. val featureScaling: Param[String]

    Feature scaling method.

    Feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

    Definition Classes
    GenericClassifierModel
  35. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  36. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  37. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  41. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  42. def getCategories(): Array[String]
    Definition Classes
    GenericClassifierModel
  43. def getCategoryName(id: Int): String
    Definition Classes
    GenericClassifierModel
  44. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. def getEncoder: GenericClassifierDataEncoder
    Definition Classes
    GenericClassifierModel
  47. def getFeatureScaling: String

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierModel
  48. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  49. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  50. def getMaxSyntacticDistance: Float

    Maximal syntactic distance, as threshold (Default: 0)

  51. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  52. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  53. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  54. def getPredictionThreshold: Float

    Minimal activation of the target unit to encode a new relation instance (Default: 0.5f)

  55. def getRelationPairs: Array[String]

    List of dash-separated pairs of named entities ("ENTITY1-ENTITY2", e.g.

    List of dash-separated pairs of named entities ("ENTITY1-ENTITY2", e.g. "Biomarker-RelativeDay"), which will be processed

  56. def getStorageRef: String
    Definition Classes
    HasStorageRef
  57. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  58. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  59. def hasParent: Boolean
    Definition Classes
    Model
  60. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  61. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  62. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator types : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

    Input annotator types : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

    Definition Classes
    RelationExtractionModel → GenericClassifierModel → HasInputAnnotationCols
  64. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  65. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  66. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  67. def isLp(): Boolean
    Definition Classes
    CheckLicense
  68. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  69. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  70. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  71. def loadModel(sparkSession: SparkSession, tfModel: TensorflowWrapper, categories: Array[String], encoder: GenericClassifierDataEncoder, nerTags: Array[String]): RelationExtractionModel.this.type
  72. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  73. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  80. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. var maxSyntacticDistance: IntParam

    Maximal syntactic distance, as threshold (Default: 0)

  85. def model: TensorflowGenericClassifier
    Definition Classes
    GenericClassifierModel
  86. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  87. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  88. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  90. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    GenericClassifierModel → ParamsAndFeaturesWritable
  91. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  92. val outputAnnotatorType: String

    Output annotator types : CATEGORY

    Output annotator types : CATEGORY

    Definition Classes
    RelationExtractionModel → GenericClassifierModel → HasOutputAnnotatorType
  93. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  94. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  95. var parent: Estimator[GenericClassifierModel]
    Definition Classes
    Model
  96. var predictionThreshold: FloatParam

    Minimal activation of the target unit to encode a new relation instance (Default: 0.5f)

  97. var relationPairs: Param[String]

    List of dash-separated pairs of named entities ("ENTITY1-ENTITY2", e.g.

    List of dash-separated pairs of named entities ("ENTITY1-ENTITY2", e.g. "Biomarker-RelativeDay"), which will be processed

  98. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  99. def scaleFeatures(features: Array[Array[Float]]): Array[Array[Float]]
    Attributes
    protected
    Definition Classes
    GenericClassifierModel
  100. def set[T](feature: StructFeature[T], value: T): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  101. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def set[T](feature: SetFeature[T], value: Set[T]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def set[T](feature: ArrayFeature[T], value: Array[T]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. final def set(paramPair: ParamPair[_]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. final def set(param: String, value: Any): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  106. final def set[T](param: Param[T], value: T): RelationExtractionModel.this.type
    Definition Classes
    Params
  107. def setCategoryNames(categoryNames: Array[String]): RelationExtractionModel.this.type
    Definition Classes
    GenericClassifierModel
  108. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  110. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. final def setDefault(paramPairs: ParamPair[_]*): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  113. final def setDefault[T](param: Param[T], value: T): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  114. def setEncoder(encoder: GenericClassifierDataEncoder): RelationExtractionModel.this.type
    Definition Classes
    GenericClassifierModel
  115. def setFeatureScaling(featureScaling: String): RelationExtractionModel.this.type

    Set the feature scaling method.

    Set the feature scaling method. Possible values are 'zscore', 'minmax' or empty (no scaling)

    Definition Classes
    GenericClassifierModel
  116. final def setInputCols(value: String*): RelationExtractionModel.this.type
    Definition Classes
    HasInputAnnotationCols
  117. final def setInputCols(value: Array[String]): RelationExtractionModel.this.type
    Definition Classes
    HasInputAnnotationCols
  118. def setLazyAnnotator(value: Boolean): RelationExtractionModel.this.type
    Definition Classes
    CanBeLazy
  119. def setMaxSyntacticDistance(maxSyntacticDistance: Int): RelationExtractionModel.this.type

    Maximal syntactic distance, as threshold (Default: 0)

  120. final def setOutputCol(value: String): RelationExtractionModel.this.type
    Definition Classes
    HasOutputAnnotationCol
  121. def setParent(parent: Estimator[GenericClassifierModel]): GenericClassifierModel
    Definition Classes
    Model
  122. def setPredictionThreshold(predictionThreshold: Float): RelationExtractionModel.this.type

    Minimal activation of the target unit to encode a new relation instance (Default: 0.5f)

  123. def setRelationPairs(relationPairs: Array[String]): RelationExtractionModel.this.type

    List of dash-separated pairs of named entities ("ENTITY1-ENTITY2", e.g.

    List of dash-separated pairs of named entities ("ENTITY1-ENTITY2", e.g. "Biomarker-RelativeDay"), which will be processed

  124. def setStorageRef(value: String): RelationExtractionModel.this.type
    Definition Classes
    HasStorageRef
  125. def setTensorflowModel(spark: SparkSession, tf: TensorflowWrapper): RelationExtractionModel.this.type
    Definition Classes
    GenericClassifierModel
  126. val storageRef: Param[String]
    Definition Classes
    HasStorageRef
  127. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  128. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  129. final def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    AnnotatorModel → Transformer
  130. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  131. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  132. final def transformSchema(schema: StructType): StructType
    Definition Classes
    RawAnnotator → PipelineStage
  133. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  134. val uid: String
    Definition Classes
    RelationExtractionModel → GenericClassifierModel → Identifiable
  135. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  136. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  137. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  138. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  139. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  140. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  141. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  142. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
    Definition Classes
    WriteTensorflowModel
  143. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
    Definition Classes
    WriteTensorflowModel
  144. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]], savedSignatures: Option[Map[String, String]]): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from GenericClassifierModel

Inherited from CheckLicense

Inherited from HasSimpleAnnotate[GenericClassifierModel]

Inherited from WriteTensorflowModel

Inherited from HasStorageRef

Inherited from AnnotatorModel[GenericClassifierModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[GenericClassifierModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[GenericClassifierModel]

Inherited from Transformer

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