Spark NLP 7.0.0 ScalaDoc - com.johnsnowlabs.nlp.annotators.re.RelationExtractionApproach

final def !=(arg0: Any): Boolean

Definition Classes: AnyRef → Any

final def ##(): Int

Definition Classes: AnyRef → Any

final def $[T](param: Param[T]): T

Attributes: protected
Definition Classes: Params

final def ==(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): GenericClassifierModel

Attributes: protected
Definition Classes: AnnotatorApproach

final def asInstanceOf[T0]: T0

Definition Classes: Any

val batchSize: IntParam

Batch size

Definition Classes: GenericClassifierParams

def beforeTraining(spark: SparkSession): Unit

Definition Classes: GenericClassifierApproach → AnnotatorApproach

final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

Attributes: protected
Definition Classes: HasInputAnnotationCols

def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String], metadata: Option[Map[String, Value]]): Unit

Definition Classes: CheckLicense

def checkValidScope(scope: String): Unit

Definition Classes: CheckLicense

def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean, metadata: Option[Map[String, Value]]): Unit

Definition Classes: CheckLicense

def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean, metadata: Option[Map[String, Value]]): Unit

Definition Classes: CheckLicense

final def clear(param: Param[_]): RelationExtractionApproach.this.type

Definition Classes: Params

def clone(): AnyRef

Attributes: protected[lang]
Definition Classes: AnyRef
Annotations: @throws( ... ) @native()

final def copy(extra: ParamMap): Estimator[GenericClassifierModel]

Definition Classes: AnnotatorApproach → Estimator → PipelineStage → Params

def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes: protected
Definition Classes: Params

var customLabels: CustomLabels

Custom relation labels

Definition Classes: RelationExtractionApproach

val datasetInfo: Param[String]

Descriptive information about the dataset being used.

Definition Classes: GenericClassifierParams

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

Attributes: protected
Definition Classes: Params

val description: String

Trains TensorFlow model for multi-class text classification

Definition Classes: RelationExtractionApproach → GenericClassifierApproach → AnnotatorApproach

val directionSensitive: BooleanParam

If it is true, only relations in the form of "ENTITY1-ENTITY2" will be considered, If it is false, both "ENTITY1-ENTITY2" and "ENTITY2-ENTITY1" relations will be considered,

Definition Classes: RelationExtractionApproach

val doExceptionHandling: BooleanParam

If true, exceptions are handled.

If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

Definition Classes: HandleExceptionParams

val dropout: FloatParam

Dropout coefficient.

It is applied to the output of the last hidden layer of the graph. Graphs without hidden layer (GenericLogRegClassifier, FewShotClassifier, GenericSVMClassifier) apply it directly to the input feature vector, so a value greater than 0 drops input features there: set it to 0 for those annotators unless input dropout is what you want.

Definition Classes: GenericClassifierParams

val epochsN: IntParam

Maximum number of epochs to train

Definition Classes: GenericClassifierParams

final def eq(arg0: AnyRef): Boolean

Definition Classes: AnyRef

def equals(arg0: Any): Boolean

Definition Classes: AnyRef → Any

def explainParam(param: Param[_]): String

Definition Classes: Params

def explainParams(): String

Definition Classes: Params

final val extraInputCols: StringArrayParam

Attributes: protected
Definition Classes: HasInputAnnotationCols

final def extractParamMap(): ParamMap

Definition Classes: Params

final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes: Params

val featureScaling: Param[String]

Feature scaling method.

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

Definition Classes: GenericClassifierParams

val filterByTokenDistance: IntParam

filtering criterion based on number of token between entities.

filtering criterion based on number of token between entities. Model only finds relations that have fewer than the specified number of tokens between them.

Definition Classes: RelationExtractionApproach

def finalize(): Unit

Attributes: protected[lang]
Definition Classes: AnyRef
Annotations: @throws( classOf[java.lang.Throwable] )

final def fit(dataset: Dataset[_]): GenericClassifierModel

Definition Classes: AnnotatorApproach → Estimator

def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GenericClassifierModel]

Definition Classes: Estimator
Annotations: @Since( "2.0.0" )

def fit(dataset: Dataset[_], paramMap: ParamMap): GenericClassifierModel

Definition Classes: Estimator
Annotations: @Since( "2.0.0" )

def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GenericClassifierModel

Definition Classes: Estimator
Annotations: @Since( "2.0.0" ) @varargs()

val fixImbalance: BooleanParam

Fix the imbalance in the training set by replicating examples of under represented categories.

The class weights are only used by the loss of graphs with a softmax output (the GenericClassifier/RelationExtraction family). Graphs with a sigmoid output (logistic regression, SVM) compute their loss without them, so this parameter has no effect there.

Definition Classes: GenericClassifierParams

val fromEntityBeginCol: Param[String]

Column for beginning of 'from' entity

Definition Classes: RelationExtractionApproach

val fromEntityEndCol: Param[String]

Column for end of 'from' entity

Definition Classes: RelationExtractionApproach

val fromEntityLabelCol: Param[String]

Column for 'from' entity label

Definition Classes: RelationExtractionApproach

final def get[T](param: Param[T]): Option[T]

Definition Classes: Params

def getBatchSize: Int

Batch size

Definition Classes: GenericClassifierParams

final def getClass(): Class[_]

Definition Classes: AnyRef → Any
Annotations: @native()

def getCustomLabels: Map[String, String]

Get custom labels

Definition Classes: RelationExtractionApproach

def getDatasetInfo: String

get descriptive information about the dataset being used

Definition Classes: GenericClassifierParams

final def getDefault[T](param: Param[T]): Option[T]

Definition Classes: Params

def getDropout: Float

Dropout coefficient

Definition Classes: GenericClassifierParams

def getExistingLabels(): Array[String]

Attributes: protected
Definition Classes: RelationExtractionApproach → GenericClassifierApproach

def getFeatureScaling: String

Get feature scaling method

Definition Classes: GenericClassifierParams

def getFixImbalance: Boolean

Fix imbalance in training set

Definition Classes: GenericClassifierParams

def getFromEntityBeginCol: String

Column for beginning of 'from' entity

Definition Classes: RelationExtractionApproach

def getFromEntityEndCol: String

Column for end of 'from' entity

Definition Classes: RelationExtractionApproach

def getFromEntityLabelCol: String

Column for 'from' entity label

Definition Classes: RelationExtractionApproach

def getGraphFolder: String

Folder with the TensorFlow graphs to choose the model graph from

Definition Classes: GenericClassifierParams

def getGraphParams(dataset: Dataset[_]): Int

def getInputCols: Array[String]

Definition Classes: HasInputAnnotationCols

def getLabelColumn: String

Column with label per each document

Definition Classes: GenericClassifierParams

def getLazyAnnotator: Boolean

Definition Classes: CanBeLazy

def getLearningRate: Float

Learning Rate

Definition Classes: GenericClassifierParams

def getMaxEpochs: Int

Maximum number of epochs to train

Definition Classes: GenericClassifierParams

def getMaxSyntacticDistance: Int

Maximal syntactic distance, as threshold (Default: 0)

Definition Classes: RelationExtractionApproach

def getModelFile: String

Path of the TensorFlow graph to train with, empty when it is resolved automatically

Definition Classes: GenericClassifierParams

def getMultiClass: Boolean

Gets the model multi class prediction mode

Definition Classes: GenericClassifierParams

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

Definition Classes: Params

final def getOutputCol: String

Definition Classes: HasOutputAnnotationCol

def getOutputLogsPath: String

Get output logs path

Definition Classes: GenericClassifierParams

def getOverrideExistingLabels: Boolean

Whether to override already learned labels when using a pretrained model to initialize the new model.

Definition Classes: RelationExtractionApproach

def getParam(paramName: String): Param[Any]

Definition Classes: Params

def getRelationDirectionCol: String

Get relation direction

Definition Classes: RelationExtractionApproach

def getScopeWindow: (Int, Int)

Get scope window

Definition Classes: RelationExtractionApproach

def getTFWrapper(inputDim: Int, outputDim: Int): TensorflowWrapper

Attributes: protected
Definition Classes: RelationExtractionApproach → GenericClassifierApproach

def getToEntityBeginCol: String

Column for beginning of 'to' entity

Definition Classes: RelationExtractionApproach

def getToEntityEndCol: String

Column for end of 'to' entity

Definition Classes: RelationExtractionApproach

def getToEntityLabelCol: String

Column for 'to' entity label

Definition Classes: RelationExtractionApproach

def getValidationSplit: Float

Choose the proportion of training dataset to be validated against the model on each Epoch.

Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

Definition Classes: GenericClassifierParams

def graphFamily: DenseClassifierGraphFamily

Family of pre-built graphs embedded in the library, used when neither modelFile nor a pretrained model is set.

Family of pre-built graphs embedded in the library, used when neither modelFile nor a pretrained model is set. Subclasses with another topology override it.

Attributes: protected
Definition Classes: GenericClassifierApproach

val graphFolder: Param[String]

Folder with TensorFlow graphs to choose the model graph from.

When it is not set, the graphs embedded in the library are used. It is ignored when modelFile is set explicitly.

Definition Classes: GenericClassifierParams

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

Definition Classes: Params

def hasParam(paramName: String): Boolean

Definition Classes: Params

def hashCode(): Int

Definition Classes: AnyRef → Any
Annotations: @native()

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes: protected
Definition Classes: Logging

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes: protected
Definition Classes: Logging

val inputAnnotatorTypes: Array[AnnotatorType]

Input annotator type : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

Definition Classes: RelationExtractionApproach → GenericClassifierApproach → HasInputAnnotationCols

final val inputCols: StringArrayParam

Attributes: protected
Definition Classes: HasInputAnnotationCols

final def isDefined(param: Param[_]): Boolean

Definition Classes: Params

final def isInstanceOf[T0]: Boolean

Definition Classes: Any

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

Definition Classes: Params

def isTraceEnabled(): Boolean

Attributes: protected
Definition Classes: Logging

val labelColumn: Param[String]

Column with label per each document

Definition Classes: GenericClassifierParams

val lazyAnnotator: BooleanParam

Definition Classes: CanBeLazy

val learningRate: FloatParam

Learning Rate

Definition Classes: GenericClassifierParams

def log: Logger

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logDebug(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logError(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logInfo(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

def logName: String

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logTrace(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

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

Attributes: protected
Definition Classes: Logging

def logWarning(msg: ⇒ String): Unit

Attributes: protected
Definition Classes: Logging

var maxSyntacticDistance: IntParam

Maximal syntactic distance, as threshold (Default: 0)

Definition Classes: RelationExtractionApproach

val modelFile: Param[String]

Path of the TensorFlow graph to train with.

Empty by default: a graph with suitable dimensions is then selected automatically, either from graphFolder or from the graphs embedded in the library. Set it to train with one specific graph.

Definition Classes: GenericClassifierParams

def msgHelper(schema: StructType): String

Attributes: protected
Definition Classes: HasInputAnnotationCols

val multiClass: BooleanParam

If multiClass is set, the model will return all the labels with corresponding scores.

If multiClass is set, the model will return all the labels with corresponding scores. By default, multiClass is false.

Definition Classes: GenericClassifierParams

final def ne(arg0: AnyRef): Boolean

Definition Classes: AnyRef

final def notify(): Unit

Definition Classes: AnyRef
Annotations: @native()

final def notifyAll(): Unit

Definition Classes: AnyRef
Annotations: @native()

def onTrained(model: GenericClassifierModel, spark: SparkSession): Unit

Definition Classes: AnnotatorApproach

val optionalInputAnnotatorTypes: Array[String]

Definition Classes: HasInputAnnotationCols

val outputAnnotatorType: String

Output annotator type : CATEGORY

Definition Classes: GenericClassifierApproach → HasOutputAnnotatorType

final val outputCol: Param[String]

Attributes: protected
Definition Classes: HasOutputAnnotationCol

val outputLogsPath: Param[String]

Folder path to save training logs.

Folder path to save training logs. If no path is specified, the logs won't be stored in disk. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

Definition Classes: GenericClassifierParams

val overrideExistingLabels: BooleanParam

Controls whether to override already learned lebels when using a pretrained model to initialize the new model.

Controls whether to override already learned lebels when using a pretrained model to initialize the new model. A value of true will override existing lab els.

Definition Classes: RelationExtractionApproach

lazy val params: Array[Param[_]]

Definition Classes: Params

val pretrainedModelPath: Param[String]

Path to an already trained RelationExtractionModel.

This pretrained model will be used as a starting point for training the new one. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

Definition Classes: RelationExtractionApproach

var relationDirectionCol: Param[String]

Relation direction column (possible values are: "none", "left" or "right").

If this parameter is not set, the model will not have direction between the relation of the entities.

Definition Classes: RelationExtractionApproach

def resolveGraphFile(inputDim: Int, outputDim: Int): String

Resolves the graph to train with.

modelFile wins when it has been set explicitly, otherwise the best graph of graphFamily is selected, either from graphFolder or from the graphs embedded in the library.

inputDim: length of the feature vectors of the training dataset
outputDim: number of classes of the training dataset

Attributes: protected
Definition Classes: GenericClassifierApproach

def resumeTraining: Boolean

Attributes: protected
Definition Classes: RelationExtractionApproach → GenericClassifierApproach

def resumeTrainingFromModel(model: RelationExtractionModel): RelationExtractionApproach.this.type

Definition Classes: RelationExtractionApproach

def save(path: String): Unit

Definition Classes: MLWritable
Annotations: @Since( "1.6.0" ) @throws( ... )

val scopeWindow: IntArrayParam

The scope window of the assertion (whole sentence by default)

Definition Classes: RelationExtractionApproach

final def set(paramPair: ParamPair[_]): RelationExtractionApproach.this.type

Attributes: protected
Definition Classes: Params

final def set(param: String, value: Any): RelationExtractionApproach.this.type

Attributes: protected
Definition Classes: Params

final def set[T](param: Param[T], value: T): RelationExtractionApproach.this.type

Definition Classes: Params

def setBatchSize(batch: Int): RelationExtractionApproach.this.type

Batch size

Definition Classes: GenericClassifierParams

def setCustomLabels(labels: Map[String, String]): RelationExtractionApproach.this.type

Set custom labels

Definition Classes: RelationExtractionApproach

def setDatasetInfo(value: String): RelationExtractionApproach.this.type

set descriptive information about the dataset being used

Definition Classes: GenericClassifierParams

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

Attributes: protected
Definition Classes: Params

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

Attributes: protected[org.apache.spark.ml]
Definition Classes: Params

def setDirectionSensitive(value: Boolean): RelationExtractionApproach.this.type

If it is true, only relations in the form of "ENTITY1-ENTITY2" will be considered, If it is false, both "ENTITY1-ENTITY2" and "ENTITY2-ENTITY1" relations will be considered,

Definition Classes: RelationExtractionApproach

def setDoExceptionHandling(value: Boolean): RelationExtractionApproach.this.type

If true, exceptions are handled.

If true, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

Definition Classes: HandleExceptionParams

def setDropout(dropout: Float): RelationExtractionApproach.this.type

Dropout coefficient

Definition Classes: GenericClassifierParams

def setEpochsNumber(epochs: Int): RelationExtractionApproach.this.type

Maximum number of epochs to train

Definition Classes: GenericClassifierParams

def setExtraInputCols(value: Array[String]): RelationExtractionApproach.this.type

Definition Classes: HasInputAnnotationCols

def setFeatureScaling(featureScaling: String): RelationExtractionApproach.this.type

Set the feature scaling method.

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

Definition Classes: GenericClassifierParams

def setFilterByTokenDistance(value: Int): RelationExtractionApproach.this.type

filtering criterion based on number of token between entities.

filtering criterion based on number of token between entities. Model only finds relations that have fewer than the specified number of tokens between them

Definition Classes: RelationExtractionApproach

def setFixImbalance(fix: Boolean): RelationExtractionApproach.this.type

Fix imbalance of training set

Definition Classes: GenericClassifierParams

def setFromEntity(beginCol: String, endCol: String, labelCol: String): RelationExtractionApproach.this.type

Set from entity

Definition Classes: RelationExtractionApproach

def setGraphFolder(path: String): RelationExtractionApproach.this.type

Set the folder with the TensorFlow graphs to choose the model graph from.

Set the folder with the TensorFlow graphs to choose the model graph from. Ignored when modelFile is set.

Definition Classes: GenericClassifierParams

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

Definition Classes: HasInputAnnotationCols

def setInputCols(value: Array[String]): RelationExtractionApproach.this.type

Definition Classes: HasInputAnnotationCols

def setLabelColumn(column: String): RelationExtractionApproach.this.type

Column with label per each document

Definition Classes: GenericClassifierParams

def setLazyAnnotator(value: Boolean): RelationExtractionApproach.this.type

Definition Classes: CanBeLazy

def setMaxSyntacticDistance(maxSyntacticDistance: Int): RelationExtractionApproach.this.type

Maximal syntactic distance, as threshold (Default: 0)

Definition Classes: RelationExtractionApproach

def setModelFile(modelFile: String): RelationExtractionApproach.this.type

Set the model file name

Definition Classes: GenericClassifierParams

def setMultiClass(value: Boolean): RelationExtractionApproach.this.type

Sets the model in multi class prediction mode

Definition Classes: GenericClassifierParams

final def setOutputCol(value: String): RelationExtractionApproach.this.type

Definition Classes: HasOutputAnnotationCol

def setOutputLogsPath(outputLogsPath: String): RelationExtractionApproach.this.type

Set the output log path

Definition Classes: GenericClassifierParams

def setOverrideExistingLabels(value: Boolean): RelationExtractionApproach.this.type

Controls whether to override already learned labels when using a pretrained model to initialize the new model.

Controls whether to override already learned labels when using a pretrained model to initialize the new model. A value of true will override existing labels.

Definition Classes: RelationExtractionApproach

def setPretrainedModelPath(path: String): RelationExtractionApproach.this.type

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

Definition Classes: RelationExtractionApproach

def setRelationDirectionCol(value: String): RelationExtractionApproach.this.type

Set relation direction column

Definition Classes: RelationExtractionApproach

def setScopeWindow(window: (Int, Int)): RelationExtractionApproach.this.type

Max possible length of a sentence.

Definition Classes: RelationExtractionApproach

def setToEntity(beginCol: String, endCol: String, labelCol: String): RelationExtractionApproach.this.type

Set to entity

Definition Classes: RelationExtractionApproach

def setValidationSplit(validationSplit: Float): RelationExtractionApproach.this.type

Choose the proportion of training dataset to be validated against the model on each Epoch.

Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.

Definition Classes: GenericClassifierParams

def setlearningRate(lr: Float): RelationExtractionApproach.this.type

Learning Rate

Definition Classes: GenericClassifierParams

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

Definition Classes: AnyRef

val toEntityBeginCol: Param[String]

Column for beginning of 'to' entity

Definition Classes: RelationExtractionApproach

val toEntityEndCol: Param[String]

Column for end of 'to' entity

Definition Classes: RelationExtractionApproach

val toEntityLabelCol: Param[String]

Column for 'to' entity label

Definition Classes: RelationExtractionApproach

def toString(): String

Definition Classes: Identifiable → AnyRef → Any

def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): RelationExtractionModel

Definition Classes: RelationExtractionApproach → GenericClassifierApproach → AnnotatorApproach

final def transformSchema(schema: StructType): StructType

Definition Classes: AnnotatorApproach → PipelineStage

def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes: protected
Definition Classes: PipelineStage
Annotations: @DeveloperApi()

val uid: String

Definition Classes: RelationExtractionApproach → GenericClassifierApproach → Identifiable

def validate(schema: StructType): Boolean

Attributes: protected
Definition Classes: AnnotatorApproach

val validationSplit: FloatParam

The proportion of training dataset to be used as validation set.

The model will be validated against this dataset on each Epoch and will not be used for training. The value should be between 0.0 and 1.0.

Definition Classes: GenericClassifierParams

final def wait(): Unit

Definition Classes: AnyRef
Annotations: @throws( ... )

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

Definition Classes: AnyRef
Annotations: @throws( ... )

final def wait(arg0: Long): Unit

Definition Classes: AnyRef
Annotations: @throws( ... ) @native()

def write: MLWriter

Definition Classes: DefaultParamsWritable → MLWritable

Packages

RelationExtractionApproach

Companion class RelationExtractionApproach

object RelationExtractionApproach extends RelationExtractionApproach

Type Members

Value Members

Inherited from RelationExtractionApproach

Inherited from GenericClassifierApproach

Inherited from CheckLicense

Inherited from HandleExceptionParams

Inherited from GenericClassifierParams

Inherited from AnnotatorApproach[GenericClassifierModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[GenericClassifierModel]

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

Members

Parameter setters

Parameter getters

Packages

RelationExtractionApproach 

Companion class RelationExtractionApproach

object RelationExtractionApproach extends RelationExtractionApproach

Type Members

Value Members

Inherited from RelationExtractionApproach

Inherited from GenericClassifierApproach

Inherited from CheckLicense

Inherited from HandleExceptionParams

Inherited from GenericClassifierParams

Inherited from AnnotatorApproach[GenericClassifierModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[GenericClassifierModel]

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

Members

Parameter setters

Parameter getters

RelationExtractionApproach