class RelationExtractionApproach extends GenericClassifierApproach with HandleExceptionParams

Trains a TensorFlow model for relation extraction.

For pretrained models, see the documentation of RelationExtractionModel.

To train a custom relation extraction model, you need to first create a Tensorflow graph using either the TfGraphBuilder annotator or the tf_graph module. Then, set the path to the Tensorflow graph using the method setModelFile.

If the parameter relationDirectionCol is set, the model will be trained using the direction information (see the parameter decription for details). Otherwise, the model won't have direction between the relation of the entities. After training a model (using the .fit() method), the resulting object is of class RelationExtractionModel.

Example

Defining pipeline stages to extract entities first

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

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

val embedder = WordEmbeddingsModel
  .pretrained("embeddings_clinical", "en", "clinical/models")
  .setInputCols(Array("document", "tokens"))
  .setOutputCol("embeddings")

val posTagger = PerceptronModel
  .pretrained("pos_clinical", "en", "clinical/models")
  .setInputCols(Array("document", "tokens"))
  .setOutputCol("posTags")

val nerTagger = MedicalNerModel
  .pretrained("ner_events_clinical", "en", "clinical/models")
  .setInputCols(Array("document", "tokens", "embeddings"))
  .setOutputCol("ner_tags")

val nerConverter = new NerConverter()
  .setInputCols(Array("document", "tokens", "ner_tags"))
  .setOutputCol("nerChunks")

val depencyParser = DependencyParserModel
  .pretrained("dependency_conllu", "en")
  .setInputCols(Array("document", "posTags", "tokens"))
  .setOutputCol("dependencies")

Then define RelationExtractionApproach and training parameters

val re = new RelationExtractionApproach()
  .setInputCols(Array("embeddings", "posTags", "train_ner_chunks", "dependencies"))
  .setOutputCol("relations_t")
  .setLabelColumn("target_rel")
  .setEpochsNumber(300)
  .setBatchSize(200)
  .setlearningRate(0.001f)
  .setModelFile("path/to/graph_file.pb")
  .setFixImbalance(true)
  .setValidationSplit(0.05f)
  .setFromEntity("from_begin", "from_end", "from_label")
  .setToEntity("to_begin", "to_end", "to_label")

val finisher = new Finisher()
  .setInputCols(Array("relations_t"))
  .setOutputCols(Array("relations"))
  .setCleanAnnotations(false)
  .setValueSplitSymbol(",")
  .setAnnotationSplitSymbol(",")
  .setOutputAsArray(false)

Define complete pipeline and start training

val pipeline = new Pipeline()
  .setStages(Array(
    documentAssembler,
    tokenizer,
    embedder,
    posTagger,
    nerTagger,
    nerConverter,
    depencyParser,
    re,
    finisher))

val model = pipeline.fit(trainData)
See also

RelationExtractionModel for pretrained models and how to use it

Linear Supertypes
GenericClassifierApproach, CheckLicense, HandleExceptionParams, AnnotatorApproach[GenericClassifierModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[GenericClassifierModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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  1. RelationExtractionApproach
  2. GenericClassifierApproach
  3. CheckLicense
  4. HandleExceptionParams
  5. AnnotatorApproach
  6. CanBeLazy
  7. DefaultParamsWritable
  8. MLWritable
  9. HasOutputAnnotatorType
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. Estimator
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. Any
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Instance Constructors

  1. new RelationExtractionApproach()
  2. new RelationExtractionApproach(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. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): GenericClassifierModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. val batchSize: IntParam

    Batch size

    Batch size

    Definition Classes
    GenericClassifierApproach
  8. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  9. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  10. def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
    Definition Classes
    CheckLicense
  11. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  12. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  13. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  14. final def clear(param: Param[_]): RelationExtractionApproach.this.type
    Definition Classes
    Params
  15. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  16. final def copy(extra: ParamMap): Estimator[GenericClassifierModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  17. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  18. var customLabels: CustomLabels

    Custom relation labels

  19. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. val description: String

    Trains TensorFlow model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    RelationExtractionApproachGenericClassifierApproach → AnnotatorApproach
  21. 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,

  22. 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
  23. val dropout: FloatParam

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierApproach
  24. val epochsN: IntParam

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierApproach
  25. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  26. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  27. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  28. def explainParams(): String
    Definition Classes
    Params
  29. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  30. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  31. val featureScaling: Param[String]

    Feature scaling method.

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

    Definition Classes
    GenericClassifierApproach
  32. 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.

  33. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  34. final def fit(dataset: Dataset[_]): GenericClassifierModel
    Definition Classes
    AnnotatorApproach → Estimator
  35. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GenericClassifierModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  36. def fit(dataset: Dataset[_], paramMap: ParamMap): GenericClassifierModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  37. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GenericClassifierModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  38. val fixImbalance: BooleanParam

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

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

    Definition Classes
    GenericClassifierApproach
  39. val fromEntityBeginCol: Param[String]

    Column for beginning of 'from' entity

  40. val fromEntityEndCol: Param[String]

    Column for end of 'from' entity

  41. val fromEntityLabelCol: Param[String]

    Column for 'from' entity label

  42. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  43. def getBatchSize: Int

    Batch size

    Batch size

    Definition Classes
    GenericClassifierApproach
  44. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. def getCustomLabels: Map[String, String]

    Get custom labels

  46. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  47. def getDropout: Float

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierApproach
  48. def getExistingLabels(): Array[String]
    Attributes
    protected
    Definition Classes
    RelationExtractionApproachGenericClassifierApproach
  49. def getFeatureScaling: String

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierApproach
  50. def getFixImbalance: Boolean

    Fix imbalance in training set

    Fix imbalance in training set

    Definition Classes
    GenericClassifierApproach
  51. def getFromEntityBeginCol: String

    Column for beginning of 'from' entity

  52. def getFromEntityEndCol: String

    Column for end of 'from' entity

  53. def getFromEntityLabelCol: String

    Column for 'from' entity label

  54. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  55. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierApproach
  56. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  57. def getLearningRate: Float

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierApproach
  58. def getMaxEpochs: Int

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierApproach
  59. def getMaxSyntacticDistance: Int

    Maximal syntactic distance, as threshold (Default: 0)

  60. def getModelFile: String

    Model file name

    Model file name

    Definition Classes
    GenericClassifierApproach
  61. def getMultiClass: Boolean

    Gets the model multi class prediction mode

    Gets the model multi class prediction mode

    Definition Classes
    GenericClassifierApproach
  62. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  63. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  64. def getOutputLogsPath: String

    Get output logs path

    Get output logs path

    Definition Classes
    GenericClassifierApproach
  65. def getOverrideExistingLabels: Boolean

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

  66. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  67. def getRelationDirectionCol: String

    Get relation direction

  68. def getTFWrapper(): TensorflowWrapper
    Attributes
    protected
    Definition Classes
    RelationExtractionApproachGenericClassifierApproach
  69. def getToEntityBeginCol: String

    Column for beginning of 'to' entity

  70. def getToEntityEndCol: String

    Column for end of 'to' entity

  71. def getToEntityLabelCol: String

    Column for 'to' entity label

  72. 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
    GenericClassifierApproach
  73. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  74. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  75. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  76. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  77. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

    Input annotator type : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

    Definition Classes
    RelationExtractionApproachGenericClassifierApproach → HasInputAnnotationCols
  79. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  80. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  81. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  82. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  83. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  84. val labelColumn: Param[String]

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierApproach
  85. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  86. val learningRate: FloatParam

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierApproach
  87. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  88. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  93. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  94. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  95. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  96. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  97. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  98. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  99. var maxSyntacticDistance: IntParam

    Maximal syntactic distance, as threshold (Default: 0)

  100. val modelFile: Param[String]

    Location of file of the model used for classification

    Location of file of the model used for classification

    Definition Classes
    GenericClassifierApproach
  101. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  102. 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
    GenericClassifierApproach
  103. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  104. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  105. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  106. def onTrained(model: GenericClassifierModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  107. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  108. val outputAnnotatorType: String

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    GenericClassifierApproach → HasOutputAnnotatorType
  109. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  110. 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
    GenericClassifierApproach
  111. 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.

  112. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  113. val pretrainedModelPath: Param[String]

    Path to an already trained RelationExtractionModel.

    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).

  114. var relationDirectionCol: Param[String]

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

    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.

  115. def resumeTraining: Boolean
    Attributes
    protected
    Definition Classes
    RelationExtractionApproachGenericClassifierApproach
  116. def resumeTrainingFromModel(model: RelationExtractionModel): RelationExtractionApproach.this.type
  117. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  118. final def set(paramPair: ParamPair[_]): RelationExtractionApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  119. final def set(param: String, value: Any): RelationExtractionApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  120. final def set[T](param: Param[T], value: T): RelationExtractionApproach.this.type
    Definition Classes
    Params
  121. def setBatchSize(batch: Int): RelationExtractionApproach.this.type

    Batch size

    Batch size

    Definition Classes
    GenericClassifierApproach
  122. def setCustomLabels(labels: Map[String, String]): RelationExtractionApproach.this.type

    Set custom labels

  123. final def setDefault(paramPairs: ParamPair[_]*): RelationExtractionApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  124. final def setDefault[T](param: Param[T], value: T): RelationExtractionApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  125. 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,

  126. 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
  127. def setDropout(dropout: Float): RelationExtractionApproach.this.type

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierApproach
  128. def setEpochsNumber(epochs: Int): RelationExtractionApproach.this.type

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierApproach
  129. 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
    GenericClassifierApproach
  130. 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

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

    Fix imbalance of training set

    Fix imbalance of training set

    Definition Classes
    GenericClassifierApproach
  132. def setFromEntity(beginCol: String, endCol: String, labelCol: String): RelationExtractionApproach.this.type

    Set from entity

  133. final def setInputCols(value: String*): RelationExtractionApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  134. def setInputCols(value: Array[String]): RelationExtractionApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  135. def setLabelColumn(column: String): RelationExtractionApproach.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierApproach
  136. def setLazyAnnotator(value: Boolean): RelationExtractionApproach.this.type
    Definition Classes
    CanBeLazy
  137. def setMaxSyntacticDistance(maxSyntacticDistance: Int): RelationExtractionApproach.this.type

    Maximal syntactic distance, as threshold (Default: 0)

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

    Set the model file name

    Set the model file name

    Definition Classes
    GenericClassifierApproach
  139. def setMultiClass(value: Boolean): RelationExtractionApproach.this.type

    Sets the model in multi class prediction mode

    Sets the model in multi class prediction mode

    Definition Classes
    GenericClassifierApproach
  140. final def setOutputCol(value: String): RelationExtractionApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  141. def setOutputLogsPath(outputLogsPath: String): RelationExtractionApproach.this.type

    Set the output log path

    Set the output log path

    Definition Classes
    GenericClassifierApproach
  142. 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.

  143. 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.

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

    Set relation direction column

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

    Set to entity

  146. 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
    GenericClassifierApproach
  147. def setlearningRate(lr: Float): RelationExtractionApproach.this.type

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierApproach
  148. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  149. val toEntityBeginCol: Param[String]

    Column for beginning of 'to' entity

  150. val toEntityEndCol: Param[String]

    Column for end of 'to' entity

  151. val toEntityLabelCol: Param[String]

    Column for 'to' entity label

  152. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  153. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): RelationExtractionModel
    Definition Classes
    RelationExtractionApproachGenericClassifierApproach → AnnotatorApproach
  154. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  155. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  156. val uid: String
    Definition Classes
    RelationExtractionApproachGenericClassifierApproach → Identifiable
  157. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  158. val validationSplit: FloatParam

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

    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
    GenericClassifierApproach
  159. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  160. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  161. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  162. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from GenericClassifierApproach

Inherited from CheckLicense

Inherited from HandleExceptionParams

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

Required input and expected output annotator types

Members

Parameter setters

Parameter getters