c

com.johnsnowlabs.nlp.annotators.resolution

SentenceEntityResolverApproach

class SentenceEntityResolverApproach extends AnnotatorApproach[SentenceEntityResolverModel] with SentenceResolverParams with HasCaseSensitiveProperties with CheckLicense

Contains all the parameters and methods to train a SentenceEntityResolverModel. The model transforms a dataset with Input Annotation type SENTENCE_EMBEDDINGS, coming from e.g. BertSentenceEmbeddings and returns the normalized entity for a particular trained ontology / curated dataset. (e.g. ICD-10, RxNorm, SNOMED etc.)

To use pretrained models please use SentenceEntityResolverModel and see the Models Hub for available models.

Example

Training a SNOMED resolution model using BERT sentence embeddings

Define pre-processing pipeline for training data. It needs consists of columns for the normalized training data and their labels.

val documentAssembler = new DocumentAssembler()
   .setInputCol("normalized_text")
   .setOutputCol("document")
 val bertEmbeddings = BertSentenceEmbeddings.pretrained("sent_biobert_pubmed_base_cased")
   .setInputCols("sentence")
   .setOutputCol("bert_embeddings")
 val snomedTrainingPipeline = new Pipeline().setStages(Array(
   documentAssembler,
   bertEmbeddings
 ))
 val snomedTrainingModel = snomedTrainingPipeline.fit(data)
 val snomedData = snomedTrainingModel.transform(data).cache()

Then the Resolver can be trained with

val bertExtractor = new SentenceEntityResolverApproach()
  .setNeighbours(25)
  .setThreshold(1000)
  .setInputCols("bert_embeddings")
  .setNormalizedCol("normalized_text")
  .setLabelCol("label")
  .setOutputCol("snomed_code")
  .setDistanceFunction("EUCLIDIAN")
  .setCaseSensitive(false)

val snomedModel = bertExtractor.fit(snomedData)
See also

SentenceEntityResolverModel

Linear Supertypes
CheckLicense, HasCaseSensitiveProperties, ParamsAndFeaturesWritable, HasFeatures, SentenceResolverParams, AnnotatorApproach[SentenceEntityResolverModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[SentenceEntityResolverModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. SentenceEntityResolverApproach
  2. CheckLicense
  3. HasCaseSensitiveProperties
  4. ParamsAndFeaturesWritable
  5. HasFeatures
  6. SentenceResolverParams
  7. AnnotatorApproach
  8. CanBeLazy
  9. DefaultParamsWritable
  10. MLWritable
  11. HasOutputAnnotatorType
  12. HasOutputAnnotationCol
  13. HasInputAnnotationCols
  14. Estimator
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

    uid

    a unique identifier for the instantiated AnnotatorModel

Type Members

  1. 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 _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): SentenceEntityResolverModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val auxLabelCol: Param[String]

    Optional column with one extra label per document.

    Optional column with one extra label per document. This extra label will be outputted later on in an additional column (Default: "aux_label")

  12. val auxLabelMap: StructFeature[Map[String, String]]
  13. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  14. val caseSensitive: BooleanParam
    Definition Classes
    HasCaseSensitiveProperties
  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[_]): SentenceEntityResolverApproach.this.type
    Definition Classes
    Params
  20. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  21. val confidenceFunction: Param[String]
    Definition Classes
    SentenceResolverParams
  22. final def copy(extra: ParamMap): Estimator[SentenceEntityResolverModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  23. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. val description: String
    Definition Classes
    SentenceEntityResolverApproach → AnnotatorApproach
  26. val distanceFunction: Param[String]

    what distance function to use for KNN: 'EUCLIDEAN' or 'COSINE'

    what distance function to use for KNN: 'EUCLIDEAN' or 'COSINE'

    Definition Classes
    SentenceResolverParams
  27. lazy val embeddingsColumnName: String
  28. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  29. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  30. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  31. def explainParams(): String
    Definition Classes
    Params
  32. def extractAuxLabelMap(dataset: Dataset[_]): Map[String, String]

    Extracts a Map[String,String] where key=label and value=auxLabel from a dataset.

    Extracts a Map[String,String] where key=label and value=auxLabel from a dataset. If either of one columns does not exist, it will return an empty map

    dataset

    from which we extract the column

    returns

    a Map[String,String]

  33. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  34. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  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. final def fit(dataset: Dataset[_]): SentenceEntityResolverModel
    Definition Classes
    AnnotatorApproach → Estimator
  38. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[SentenceEntityResolverModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  39. def fit(dataset: Dataset[_], paramMap: ParamMap): SentenceEntityResolverModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  40. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SentenceEntityResolverModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  41. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  42. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  43. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  44. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  45. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. def getAuxLabelCol(): Option[String]

    Optional column with one extra label per document.

    Optional column with one extra label per document. This extra label will be outputted later on in an additional column

  47. def getAuxLabelMap(): Map[String, String]

    Map[String,String] where key=label and value=auxLabel from a dataset.

  48. def getCaseSensitive: Boolean
    Definition Classes
    HasCaseSensitiveProperties
  49. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  50. def getConfidenceFunction: String
    Definition Classes
    SentenceResolverParams
  51. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  52. def getDistanceFunction: String
    Definition Classes
    SentenceResolverParams
  53. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  54. def getLabelCol: String

    column name for the value we are trying to resolve

  55. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  56. def getMissAsEmpty: Boolean
    Definition Classes
    SentenceResolverParams
  57. def getNeighbours: Int
    Definition Classes
    SentenceResolverParams
  58. def getNormalizedCol: String

    column name for the original, normalized description

  59. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  60. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  61. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  62. def getReturnAllKEmbeddings(): Boolean

    Whether to return all embeddings of all K candidates of the resolution.

    Whether to return all embeddings of all K candidates of the resolution. Embeddings will be in the metadata. Increase in RAM usage to be expected

  63. def getReturnCosineDistances: Boolean

    Whether to calculate and return cosine distances between a sentence and the k closest candidates.

    Whether to calculate and return cosine distances between a sentence and the k closest candidates. Can improve accuracy but increases computation.

  64. def getThreshold: Double
    Definition Classes
    SentenceResolverParams
  65. def getUseAuxLabel(): Boolean

    Whether to use Aux Label or not

  66. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  67. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  68. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  69. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  70. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. val inputAnnotatorTypes: Array[String]

    Input annotator types: SENTENCE_EMBEDDINGS

    Input annotator types: SENTENCE_EMBEDDINGS

    Definition Classes
    SentenceEntityResolverApproach → HasInputAnnotationCols
  72. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  73. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  74. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  75. def isLp(): Boolean
    Definition Classes
    CheckLicense
  76. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  77. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  78. val labelCol: Param[String]

    column name for the value we are trying to resolve (Default: "code")

  79. lazy val labelColumnName: String
  80. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  81. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  82. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  89. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  93. val missAsEmpty: BooleanParam

    whether or not to return an empty annotation on unmatched chunks

    whether or not to return an empty annotation on unmatched chunks

    Definition Classes
    SentenceResolverParams
  94. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  95. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  96. val neighbours: IntParam

    number of neighbours to consider in the KNN query to calculate WMD

    number of neighbours to consider in the KNN query to calculate WMD

    Definition Classes
    SentenceResolverParams
  97. val normalizedCol: Param[String]

    column name for the original, normalized description

  98. lazy val normalizedColumnName: String
  99. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  100. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  101. def onTrained(model: SentenceEntityResolverModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  102. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  103. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  104. val outputAnnotatorType: AnnotatorType

    Output annotator types: ENTITY

    Output annotator types: ENTITY

    Definition Classes
    SentenceEntityResolverApproach → HasOutputAnnotatorType
  105. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  106. val overrideExistingCodes: BooleanParam

    Whether to override the existing codes with new data while continue the training from a pretrained model.

    Whether to override the existing codes with new data while continue the training from a pretrained model. Default value is false(keep all the codes).

  107. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  108. val pretrainedModelPath: Param[String]

    Path to an already trained SentenceEntityResolverModel, which is used as a starting point for training the new model.

  109. val returnAllKEmbeddings: BooleanParam

    Whether to return all embeddings of all K candidates of the resolution.

    Whether to return all embeddings of all K candidates of the resolution. Embeddings will be in the metadata. Increase in RAM usage to be expected (Default: false)

  110. val returnCosineDistances: BooleanParam

    Whether to calculate and return cosine distances between a sentence and the k closest candidates.

    Whether to calculate and return cosine distances between a sentence and the k closest candidates. Can improve accuracy but increases computation (Default: true)

  111. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  112. def set[T](feature: StructFeature[T], value: T): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  114. def set[T](feature: SetFeature[T], value: Set[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  115. def set[T](feature: ArrayFeature[T], value: Array[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  116. final def set(paramPair: ParamPair[_]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  117. final def set(param: String, value: Any): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  118. final def set[T](param: Param[T], value: T): SentenceEntityResolverApproach.this.type
    Definition Classes
    Params
  119. def setAuxLabelCol(c: String): SentenceEntityResolverApproach.this.type

    Optional column with one extra label per document.

    Optional column with one extra label per document. This extra label will be outputted later on in an additional column

  120. def setAuxLabelMap(m: Map[String, String]): SentenceEntityResolverApproach.this.type

    Map[String,String] where key=label and value=auxLabel from a dataset.

  121. def setCaseSensitive(value: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasCaseSensitiveProperties
  122. def setConfidenceFunction(v: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  123. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  124. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  125. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  126. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  127. final def setDefault(paramPairs: ParamPair[_]*): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  128. final def setDefault[T](param: Param[T], value: T): SentenceEntityResolverApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  129. def setDistanceFunction(value: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  130. final def setInputCols(value: String*): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  131. final def setInputCols(value: Array[String]): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  132. def setLabelCol(value: String): SentenceEntityResolverApproach.this.type

    column name for the value we are trying to resolve

  133. def setLazyAnnotator(value: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    CanBeLazy
  134. def setMissAsEmpty(v: Boolean): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  135. def setNeighbours(k: Int): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  136. def setNormalizedCol(value: String): SentenceEntityResolverApproach.this.type

    column name for the original, normalized description

  137. final def setOutputCol(value: String): SentenceEntityResolverApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  138. def setPretrainedModelPath(path: String): SentenceEntityResolverApproach.this.type

    Set the location of an already trained SentenceEntityResolverModel, which is used as a starting point for training the new model.

  139. def setReturnAllKEmbeddings(b: Boolean): SentenceEntityResolverApproach.this.type

    Whether to return all embeddings of all K candidates of the resolution.

    Whether to return all embeddings of all K candidates of the resolution. Embeddings will be in the metadata. Increase in RAM usage to be expected

  140. def setReturnCosineDistances(value: Boolean): SentenceEntityResolverApproach.this.type

    Whether to calculate and return cosine distances between a sentence and the k closest candidates.

    Whether to calculate and return cosine distances between a sentence and the k closest candidates. Can improve accuracy but increases computation.

  141. def setThreshold(dist: Double): SentenceEntityResolverApproach.this.type
    Definition Classes
    SentenceResolverParams
  142. def setUseAuxLabel(b: Boolean): SentenceEntityResolverApproach.this.type

    Whether to use Aux Label or not

  143. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  144. val threshold: DoubleParam

    threshold value for the aggregated distance

    threshold value for the aggregated distance

    Definition Classes
    SentenceResolverParams
  145. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  146. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): SentenceEntityResolverModel

    Returns the SentenceEntityResolverModel Transformer, that can be used to transform input datasets

    Returns the SentenceEntityResolverModel Transformer, that can be used to transform input datasets

    The dataset provided to the fit method should have one sentence per row and contain the following columns: SentenceEmbeddings, ResolverLabel, ResolverNormalized

    The cardinality of the dataset should not exceed 100.000 data points since searching in such a big KD-tree becomes impractical

    This method is called inside the AnnotatorApproach's fit method

    dataset

    a Dataset containing SentenceEmbeddings, ResolverLabel, ResolverNormalized

    returns

    a trained SentenceEntityResolverModel

    Definition Classes
    SentenceEntityResolverApproach → AnnotatorApproach
  147. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  148. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  149. val uid: String
    Definition Classes
    SentenceEntityResolverApproach → Identifiable
  150. val useAuxLabel: BooleanParam

    Whether to use Aux Label or not (Default: false)

  151. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  152. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  153. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  154. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  155. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from CheckLicense

Inherited from HasCaseSensitiveProperties

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from SentenceResolverParams

Inherited from AnnotatorApproach[SentenceEntityResolverModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[SentenceEntityResolverModel]

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