class RelationExtractionModel extends GenericClassifierModel with ParamsAndFeaturesWritable with HandleExceptionParams with HasSafeAnnotate[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
HasSafeAnnotate[GenericClassifierModel], HandleExceptionParams, 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
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Inherited
  1. RelationExtractionModel
  2. HasSafeAnnotate
  3. HandleExceptionParams
  4. GenericClassifierModel
  5. CheckLicense
  6. HasSimpleAnnotate
  7. WriteTensorflowModel
  8. HasStorageRef
  9. AnnotatorModel
  10. CanBeLazy
  11. RawAnnotator
  12. HasOutputAnnotationCol
  13. HasInputAnnotationCols
  14. HasOutputAnnotatorType
  15. ParamsAndFeaturesWritable
  16. HasFeatures
  17. DefaultParamsWritable
  18. MLWritable
  19. Model
  20. Transformer
  21. PipelineStage
  22. Logging
  23. Params
  24. Serializable
  25. Serializable
  26. Identifiable
  27. AnyRef
  28. 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
    RelationExtractionModelGenericClassifierModel → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    GenericClassifierModel → AnnotatorModel
  14. def categorizeRel(relation: RelationInstance): (Long, Float, Array[Float])
    Attributes
    protected
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
    Definition Classes
    CheckLicense
  17. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  18. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  19. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  20. val classes: StringArrayParam
    Definition Classes
    GenericClassifierModel
  21. final def clear(param: Param[_]): RelationExtractionModel.this.type
    Definition Classes
    Params
  22. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  23. def copy(extra: ParamMap): GenericClassifierModel
    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  24. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  26. var customLabels: MapFeature[String, String]

    Custom relation labels

  27. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  28. def dfAnnotate: UserDefinedFunction
    Definition Classes
    HasSimpleAnnotate
  29. 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
  30. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  32. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  33. def explainParams(): String
    Definition Classes
    Params
  34. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  35. def extraValidateMsg: String
    Attributes
    protected
    Definition Classes
    RawAnnotator
  36. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  37. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  38. val featureScaling: Param[String]

    Feature scaling method.

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

    Definition Classes
    GenericClassifierModel
  39. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  40. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  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 getCategories(): Array[String]
    Definition Classes
    GenericClassifierModel
  47. def getCategoryName(id: Int): String
    Definition Classes
    GenericClassifierModel
  48. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  49. def getClasses: Array[String]

    Proxy to getCategories

  50. def getCustomLabel(label: String): String
  51. def getCustomLabels: Map[String, String]

    Custom relation labels

  52. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  53. def getEncoder: GenericClassifierDataEncoder
    Definition Classes
    GenericClassifierModel
  54. def getFeatureScaling: String

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierModel
  55. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  56. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  57. def getMaxSyntacticDistance: Int

    Maximal syntactic distance, as threshold (Default: 0)

  58. def getMultiClass: Boolean

    Gets the model multi class prediction mode

    Gets the model multi class prediction mode

    Definition Classes
    GenericClassifierModel
  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 getPredictionThreshold: Float

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

  63. 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

  64. def getRelationPairsCaseSensitive: Boolean

    Gets the case sensitivity of relation pairs

  65. def getRelationTypePerPair: Map[String, Array[String]]

    Get the lists of entity pairs allowed for a given relation

  66. def getRelationTypePerPairStr: String

    Get a string representation of the lists of entity pairs allowed for a given relation

  67. def getStorageRef: String
    Definition Classes
    HasStorageRef
  68. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  69. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  70. def hasParent: Boolean
    Definition Classes
    Model
  71. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  72. val inExceptionMode: Boolean
    Attributes
    protected
    Definition Classes
    HasSafeAnnotate
  73. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  74. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator types : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

    Input annotator types : WORD_EMBEDDINGS, POS, CHUNK, DEPENDENCY

    Definition Classes
    RelationExtractionModelGenericClassifierModel → HasInputAnnotationCols
  76. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  77. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  78. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  79. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  80. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  81. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  82. def loadModel(sparkSession: SparkSession, tfModel: TensorflowWrapper, categories: Array[String], encoder: GenericClassifierDataEncoder, nerTags: Array[String]): RelationExtractionModel.this.type
  83. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  84. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  91. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  93. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  94. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  95. var maxSyntacticDistance: IntParam

    Maximal syntactic distance, as threshold (Default: 0)

  96. def model: TensorflowGenericClassifier
    Definition Classes
    GenericClassifierModel
  97. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  98. var 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
    GenericClassifierModel
  99. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  100. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  101. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  102. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    GenericClassifierModel → ParamsAndFeaturesWritable
  103. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  104. val outputAnnotatorType: String

    Output annotator types : CATEGORY

    Output annotator types : CATEGORY

    Definition Classes
    RelationExtractionModelGenericClassifierModel → HasOutputAnnotatorType
  105. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  106. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  107. var parent: Estimator[GenericClassifierModel]
    Definition Classes
    Model
  108. var predictionThreshold: FloatParam

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

  109. 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

  110. var relationPairsCaseSensitive: BooleanParam

    Determines whether relation pairs are case sensitive

  111. def safeAnnotate(annotations: Seq[Annotation]): Seq[Annotation]

    A protected method designed to safely annotate a sequence of Annotation objects by handling exceptions.

    A protected method designed to safely annotate a sequence of Annotation objects by handling exceptions.

    annotations

    A sequence of Annotation.

    returns

    A sequence of Annotation objects after processing, potentially containing error annotations.

    Attributes
    protected
    Definition Classes
    HasSafeAnnotate
  112. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  113. def scaleFeatures(features: Array[Array[Float]]): Array[Array[Float]]
    Attributes
    protected
    Definition Classes
    GenericClassifierModel
  114. def set[T](feature: StructFeature[T], value: T): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  115. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  116. def set[T](feature: SetFeature[T], value: Set[T]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  117. def set[T](feature: ArrayFeature[T], value: Array[T]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  118. final def set(paramPair: ParamPair[_]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  119. final def set(param: String, value: Any): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  120. final def set[T](param: Param[T], value: T): RelationExtractionModel.this.type
    Definition Classes
    Params
  121. def setCategoryNames(categoryNames: Array[String]): RelationExtractionModel.this.type
    Definition Classes
    GenericClassifierModel
  122. def setCustomLabels(labels: HashMap[String, String]): RelationExtractionModel.this.type

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

    Set custom relation labels

  124. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  125. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  126. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  127. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  128. final def setDefault(paramPairs: ParamPair[_]*): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  129. final def setDefault[T](param: Param[T], value: T): RelationExtractionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  130. def setDoExceptionHandling(value: Boolean): RelationExtractionModel.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
  131. def setEncoder(encoder: GenericClassifierDataEncoder): RelationExtractionModel.this.type
    Definition Classes
    GenericClassifierModel
  132. 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
  133. final def setInputCols(value: String*): RelationExtractionModel.this.type
    Definition Classes
    HasInputAnnotationCols
  134. def setInputCols(value: Array[String]): RelationExtractionModel.this.type
    Definition Classes
    HasInputAnnotationCols
  135. def setLazyAnnotator(value: Boolean): RelationExtractionModel.this.type
    Definition Classes
    CanBeLazy
  136. def setMaxSyntacticDistance(maxSyntacticDistance: Int): RelationExtractionModel.this.type

    Maximal syntactic distance, as threshold (Default: 0)

  137. def setMultiClass(value: Boolean): RelationExtractionModel.this.type

    Sets the model in multi class prediction mode

    Sets the model in multi class prediction mode

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

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

  141. 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

  142. def setRelationPairsCaseSensitive(value: Boolean): RelationExtractionModel.this.type

    Sets the case sensitivity of relation pairs

  143. def setRelationTypePerPair(categories: HashMap[String, List[String]]): RelationExtractionModel.this.type

    Set the lists of entity pairs allowed for a given relation

  144. def setRelationTypePerPair(categories: Map[String, Array[String]]): RelationExtractionModel.this.type

    Set the lists of entity pairs allowed for a given relation

  145. def setStorageRef(value: String): RelationExtractionModel.this.type
    Definition Classes
    HasStorageRef
  146. def setTensorflowModel(spark: SparkSession, tf: TensorflowWrapper): RelationExtractionModel.this.type
    Definition Classes
    GenericClassifierModel
  147. val storageRef: Param[String]
    Definition Classes
    HasStorageRef
  148. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  149. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  150. final def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    AnnotatorModel → Transformer
  151. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  152. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  153. final def transformSchema(schema: StructType): StructType
    Definition Classes
    RawAnnotator → PipelineStage
  154. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  155. val uid: String
    Definition Classes
    RelationExtractionModelGenericClassifierModel → Identifiable
  156. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  157. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  158. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  159. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  160. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  161. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  162. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  163. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
    Definition Classes
    WriteTensorflowModel
  164. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
    Definition Classes
    WriteTensorflowModel
  165. 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 HandleExceptionParams

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