object FewShotAssertionClassifierApproach extends FewShotAssertionClassifierApproach

Linear Supertypes
FewShotAssertionClassifierApproach, WhiteAndBlackListParams, GenericClassifierApproach, CheckLicense, HandleExceptionParams, GenericClassifierParams, AnnotatorApproach[GenericClassifierModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[GenericClassifierModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. FewShotAssertionClassifierApproach
  2. FewShotAssertionClassifierApproach
  3. WhiteAndBlackListParams
  4. GenericClassifierApproach
  5. CheckLicense
  6. HandleExceptionParams
  7. GenericClassifierParams
  8. AnnotatorApproach
  9. CanBeLazy
  10. DefaultParamsWritable
  11. MLWritable
  12. HasOutputAnnotatorType
  13. HasOutputAnnotationCol
  14. HasInputAnnotationCols
  15. Estimator
  16. PipelineStage
  17. Logging
  18. Params
  19. Serializable
  20. Serializable
  21. Identifiable
  22. AnyRef
  23. Any
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Visibility
  1. Public
  2. All

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
    GenericClassifierParams
  8. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  9. val blackList: StringArrayParam

    If defined, list of entities to ignore.

    If defined, list of entities to ignore. The rest will be processed. Should not include IOB prefix on labels. Default: Array()

    Definition Classes
    WhiteAndBlackListParams
  10. val caseSensitive: BooleanParam

    Determines whether the definitions of the white listed and black listed entities are case sensitive or not.

    Determines whether the definitions of the white listed and black listed entities are case sensitive or not. Default: true

    Definition Classes
    WhiteAndBlackListParams
  11. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  12. def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String], metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  13. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  14. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean, metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  15. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean, metadata: Option[Map[String, Value]]): Unit
    Definition Classes
    CheckLicense
  16. final def clear(param: Param[_]): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    Params
  17. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  18. final def copy(extra: ParamMap): Estimator[GenericClassifierModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  19. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. val datasetInfo: Param[String]

    Descriptive information about the dataset being used.

    Descriptive information about the dataset being used.

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

    Trains TensorFlow model for multi-class text classification

    Trains TensorFlow model for multi-class text classification

    Definition Classes
    GenericClassifierApproach → AnnotatorApproach
  23. 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
  24. val dropout: FloatParam

    Dropout coefficient.

    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
  25. val epochsN: IntParam

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. def evaluateFilter(filter: String): Boolean

    Filter annotations by blackList and whiteList, taking into account the caseSensitive param.

    Filter annotations by blackList and whiteList, taking into account the caseSensitive param.

    Attributes
    protected
    Definition Classes
    WhiteAndBlackListParams
  29. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  30. def explainParams(): String
    Definition Classes
    Params
  31. final val extraInputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  32. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  33. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  34. val featureScaling: Param[String]

    Feature scaling method.

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

    Definition Classes
    GenericClassifierParams
  35. def filterByEntityField(annotation: Annotation): Boolean

    Filter annotation by blackList and whiteList, taking into account the caseSensitive param.

    Filter annotation by blackList and whiteList, taking into account the caseSensitive param. It filters by annotation.metadata.getOrElse("entity", annotation.metadata.getOrElse("identifier", "")).toString

    returns

    Boolean

    Attributes
    protected
    Definition Classes
    WhiteAndBlackListParams
  36. def filterByEntityField(annotations: Seq[Annotation]): Seq[Annotation]

    Filter annotations by blackList and whiteList, taking into account the caseSensitive param.

    Filter annotations by blackList and whiteList, taking into account the caseSensitive param. It filters by annotation.metadata.getOrElse("entity", annotation.metadata.getOrElse("identifier", "")).toString

    Attributes
    protected
    Definition Classes
    WhiteAndBlackListParams
  37. def filterByWhiteAndBlackList(annotation: Annotation): Boolean

    Filter annotation by blackList and whiteList, taking into account the caseSensitive param.

    Filter annotation by blackList and whiteList, taking into account the caseSensitive param. It filters by annotation.result

    returns

    Boolean

    Attributes
    protected
    Definition Classes
    WhiteAndBlackListParams
  38. def filterByWhiteAndBlackList(annotations: Seq[Annotation]): Seq[Annotation]

    Filter annotations by blackList and whiteList, taking into account the caseSensitive param.

    Filter annotations by blackList and whiteList, taking into account the caseSensitive param. It filters by annotation.result

    Attributes
    protected
    Definition Classes
    WhiteAndBlackListParams
  39. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  40. final def fit(dataset: Dataset[_]): GenericClassifierModel
    Definition Classes
    AnnotatorApproach → Estimator
  41. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GenericClassifierModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  42. def fit(dataset: Dataset[_], paramMap: ParamMap): GenericClassifierModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  43. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GenericClassifierModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  44. 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.

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

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  47. def getBlackList: Array[String]

    Gets blackList param

    Gets blackList param

    Definition Classes
    WhiteAndBlackListParams
  48. def getCaseSensitive: Boolean

    Gets caseSensitive param

    Gets caseSensitive param

    Definition Classes
    WhiteAndBlackListParams
  49. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  50. def getDatasetInfo: String

    get descriptive information about the dataset being used

    get descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  51. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  52. def getDropout: Float

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  53. def getExistingLabels(): Array[String]
    Attributes
    protected
    Definition Classes
    GenericClassifierApproach
  54. def getFeatureScaling: String

    Get feature scaling method

    Get feature scaling method

    Definition Classes
    GenericClassifierParams
  55. def getFixImbalance: Boolean

    Fix imbalance in training set

    Fix imbalance in training set

    Definition Classes
    GenericClassifierParams
  56. def getGraphFolder: String

    Folder with the TensorFlow graphs to choose the model graph from

    Folder with the TensorFlow graphs to choose the model graph from

    Definition Classes
    GenericClassifierParams
  57. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  58. def getLabelColumn: String

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  59. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  60. def getLearningRate: Float

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  61. def getMaxEpochs: Int

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  62. def getModelFile: String

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

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

    Definition Classes
    GenericClassifierParams
  63. def getMultiClass: Boolean

    Gets the model multi class prediction mode

    Gets the model multi class prediction mode

    Definition Classes
    GenericClassifierParams
  64. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  65. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  66. def getOutputLogsPath: String

    Get output logs path

    Get output logs path

    Definition Classes
    GenericClassifierParams
  67. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  68. def getTFWrapper(inputDim: Int, outputDim: Int): TensorflowWrapper
    Attributes
    protected
    Definition Classes
    GenericClassifierApproach
  69. 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
  70. def getWhiteList: Array[String]

    Gets whiteList param

    Gets whiteList param

    Definition Classes
    WhiteAndBlackListParams
  71. 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
  72. val graphFolder: Param[String]

    Folder with TensorFlow graphs to choose the model graph from.

    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
  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 types : SENTENCE_EMBEDDINGS

    Input annotator types : SENTENCE_EMBEDDINGS

    Definition Classes
    FewShotAssertionClassifierApproach → GenericClassifierApproach → 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. def isValueInList(value: String, list: Array[String]): Boolean
    Attributes
    protected
    Definition Classes
    WhiteAndBlackListParams
  85. def isWhiteListAndBlacklistEmpty: Boolean
    Attributes
    protected
    Definition Classes
    WhiteAndBlackListParams
  86. val labelColumn: Param[String]

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  87. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  88. val learningRate: FloatParam

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  89. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  90. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  93. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  94. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  95. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  96. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  97. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  98. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  99. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  100. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  101. val modelFile: Param[String]

    Path of the TensorFlow graph to train with.

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

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    GenericClassifierApproach → HasOutputAnnotatorType
  110. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  111. 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
  112. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  113. def resolveGraphFile(inputDim: Int, outputDim: Int): String

    Resolves the graph to train with.

    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
  114. def resumeTraining: Boolean
    Attributes
    protected
    Definition Classes
    GenericClassifierApproach
  115. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  116. final def set(paramPair: ParamPair[_]): FewShotAssertionClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  117. final def set(param: String, value: Any): FewShotAssertionClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  118. final def set[T](param: Param[T], value: T): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    Params
  119. def setAllowList(list: String*): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    WhiteAndBlackListParams
  120. def setAllowList(list: Array[String]): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    WhiteAndBlackListParams
  121. def setBatchSize(batch: Int): FewShotAssertionClassifierApproach.this.type

    Batch size

    Batch size

    Definition Classes
    GenericClassifierParams
  122. def setBlackList(list: String*): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    WhiteAndBlackListParams
  123. def setBlackList(list: Array[String]): FewShotAssertionClassifierApproach.this.type

    If defined, list of entities to ignore.

    If defined, list of entities to ignore. The rest will be processed. Should not include IOB prefix on labels. Default: Array()

    Definition Classes
    WhiteAndBlackListParams
  124. def setCaseSensitive(value: Boolean): FewShotAssertionClassifierApproach.this.type

    Determines whether the definitions of the white listed and black listed entities are case sensitive or not.

    Determines whether the definitions of the white listed and black listed entities are case sensitive or not. Default: true

    Definition Classes
    WhiteAndBlackListParams
  125. def setDatasetInfo(value: String): FewShotAssertionClassifierApproach.this.type

    set descriptive information about the dataset being used

    set descriptive information about the dataset being used

    Definition Classes
    GenericClassifierParams
  126. final def setDefault(paramPairs: ParamPair[_]*): FewShotAssertionClassifierApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  127. final def setDefault[T](param: Param[T], value: T): FewShotAssertionClassifierApproach.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  128. def setDenyList(list: String*): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    WhiteAndBlackListParams
  129. def setDenyList(list: Array[String]): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    WhiteAndBlackListParams
  130. def setDoExceptionHandling(value: Boolean): FewShotAssertionClassifierApproach.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 setDropout(dropout: Float): FewShotAssertionClassifierApproach.this.type

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    GenericClassifierParams
  132. def setEpochsNumber(epochs: Int): FewShotAssertionClassifierApproach.this.type

    Maximum number of epochs to train

    Maximum number of epochs to train

    Definition Classes
    GenericClassifierParams
  133. def setExtraInputCols(value: Array[String]): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  134. def setFeatureScaling(featureScaling: String): FewShotAssertionClassifierApproach.this.type

    Set the feature scaling method.

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

    Definition Classes
    GenericClassifierParams
  135. def setFixImbalance(fix: Boolean): FewShotAssertionClassifierApproach.this.type

    Fix imbalance of training set

    Fix imbalance of training set

    Definition Classes
    GenericClassifierParams
  136. def setGraphFolder(path: String): FewShotAssertionClassifierApproach.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
  137. final def setInputCols(value: String*): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  138. def setInputCols(value: Array[String]): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  139. def setLabelColumn(column: String): FewShotAssertionClassifierApproach.this.type

    Column with label per each document

    Column with label per each document

    Definition Classes
    GenericClassifierParams
  140. def setLazyAnnotator(value: Boolean): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    CanBeLazy
  141. def setModelFile(modelFile: String): FewShotAssertionClassifierApproach.this.type

    Set the model file name

    Set the model file name

    Definition Classes
    GenericClassifierParams
  142. def setMultiClass(value: Boolean): FewShotAssertionClassifierApproach.this.type

    Sets the model in multi class prediction mode

    Sets the model in multi class prediction mode

    Definition Classes
    GenericClassifierParams
  143. final def setOutputCol(value: String): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  144. def setOutputLogsPath(outputLogsPath: String): FewShotAssertionClassifierApproach.this.type

    Set the output log path

    Set the output log path

    Definition Classes
    GenericClassifierParams
  145. def setValidationSplit(validationSplit: Float): FewShotAssertionClassifierApproach.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
  146. def setWhiteList(list: String*): FewShotAssertionClassifierApproach.this.type
    Definition Classes
    WhiteAndBlackListParams
  147. def setWhiteList(list: Array[String]): FewShotAssertionClassifierApproach.this.type

    Sets the list of entities to process.

    Sets the list of entities to process. The rest will be ignored. Should not include IOB prefix on labels. Default: Array()

    Definition Classes
    WhiteAndBlackListParams
  148. def setlearningRate(lr: Float): FewShotAssertionClassifierApproach.this.type

    Learning Rate

    Learning Rate

    Definition Classes
    GenericClassifierParams
  149. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  150. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  151. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): FewShotAssertionClassifierModel
    Definition Classes
    FewShotAssertionClassifierApproach → GenericClassifierApproach → AnnotatorApproach
  152. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  153. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  154. val uid: String
    Definition Classes
    FewShotAssertionClassifierApproach → GenericClassifierApproach → Identifiable
  155. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  156. 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
    GenericClassifierParams
  157. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  158. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  159. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  160. val whiteList: StringArrayParam

    If defined, list of entities to process.

    If defined, list of entities to process. The rest will be ignored. Should not include IOB prefix on labels. Default: Array()

    Definition Classes
    WhiteAndBlackListParams
  161. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from FewShotAssertionClassifierApproach

Inherited from WhiteAndBlackListParams

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

Required input and expected output annotator types

Members

Parameter setters

Parameter getters