class MedicalNerModel extends AnnotatorModel[MedicalNerModel] with HasBatchedAnnotate[MedicalNerModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable with Licensed

Linear Supertypes
Licensed, HasStorageRef, WriteTensorflowModel, HasBatchedAnnotate[MedicalNerModel], AnnotatorModel[MedicalNerModel], CanBeLazy, RawAnnotator[MedicalNerModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[MedicalNerModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. MedicalNerModel
  2. Licensed
  3. HasStorageRef
  4. WriteTensorflowModel
  5. HasBatchedAnnotate
  6. AnnotatorModel
  7. CanBeLazy
  8. RawAnnotator
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. HasOutputAnnotatorType
  12. ParamsAndFeaturesWritable
  13. HasFeatures
  14. DefaultParamsWritable
  15. MLWritable
  16. Model
  17. Transformer
  18. PipelineStage
  19. Logging
  20. Params
  21. Serializable
  22. Serializable
  23. Identifiable
  24. AnyRef
  25. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  12. def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]
    Definition Classes
    MedicalNerModel → HasBatchedAnnotate
  13. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotate
  14. val batchSize: IntParam
    Definition Classes
    HasBatchedAnnotate
  15. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    MedicalNerModel → AnnotatorModel
  16. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  17. val classes: StringArrayParam
  18. final def clear(param: Param[_]): MedicalNerModel.this.type
    Definition Classes
    Params
  19. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  20. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  21. def copy(extra: ParamMap): MedicalNerModel
    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  24. val datasetParams: StructFeature[DatasetEncoderParams]

    datasetParams

  25. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  29. def explainParams(): String
    Definition Classes
    Params
  30. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  31. def extraValidateMsg: String
    Attributes
    protected
    Definition Classes
    RawAnnotator
  32. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  33. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  34. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  35. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  36. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  37. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  41. def getBatchSize: Int
    Definition Classes
    HasBatchedAnnotate
  42. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  43. def getClasses: Array[String]

    get the tags used to trained this MedicalNerModel

  44. def getConfigProtoBytes: Option[Array[Byte]]

    datasetParams

  45. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. def getIncludeAllConfidenceScores: Boolean

    whether to include all confidence scores in annotation metadata or just the score of the predicted tag

  47. def getIncludeConfidence: Boolean

    whether to include confidence scores in annotation metadata

  48. def getInferenceBatchSize: Int

    get the number of sentences to process in a single batch during inference

  49. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  50. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  51. def getMinProba: Float

    Minimum probability.

    Minimum probability. Used only if there is no CRF on top of LSTM layer.

  52. def getModelIfNotSet: TensorflowMedicalNer

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  53. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  54. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  55. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  56. def getStorageRef: String
    Definition Classes
    HasStorageRef
  57. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  58. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  59. def hasParent: Boolean
    Definition Classes
    Model
  60. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  61. val includeAllConfidenceScores: BooleanParam

    whether to include all confidence scores in annotation metadata or just score of the predicted tag

  62. val includeConfidence: BooleanParam

    whether to include confidence scores in annotation metadata

  63. val inferenceBatchSize: IntParam

    Number of sentences to process in a single batch during inference

  64. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  65. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. val inputAnnotatorTypes: Array[String]

    Required input Annotators coulumns, expects DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Required input Annotators coulumns, expects DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Definition Classes
    MedicalNerModel → HasInputAnnotationCols
  67. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  68. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  69. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  70. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  71. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  72. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  73. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  74. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  81. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. val minProba: FloatParam

    Minimum probability.

    Minimum probability. Used only if there is no CRF on top of LSTM layer.

  86. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  87. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  88. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  90. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    MedicalNerModel → ParamsAndFeaturesWritable
  91. val outputAnnotatorType: String

    Output Annnotator type : NAMED_ENTITY

    Output Annnotator type : NAMED_ENTITY

    Definition Classes
    MedicalNerModel → HasOutputAnnotatorType
  92. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  93. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  94. var parent: Estimator[MedicalNerModel]
    Definition Classes
    Model
  95. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  96. def set[T](feature: StructFeature[T], value: T): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  98. def set[T](feature: SetFeature[T], value: Set[T]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  99. def set[T](feature: ArrayFeature[T], value: Array[T]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  100. final def set(paramPair: ParamPair[_]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  101. final def set(param: String, value: Any): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  102. final def set[T](param: Param[T], value: T): MedicalNerModel.this.type
    Definition Classes
    Params
  103. def setBatchSize(size: Int): MedicalNerModel.this.type
    Definition Classes
    HasBatchedAnnotate
  104. def setConfigProtoBytes(bytes: Array[Int]): MedicalNerModel.this.type

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  105. def setDatasetParams(params: DatasetEncoderParams): MedicalNerModel.this.type

    datasetParams

  106. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  107. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  108. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  110. final def setDefault(paramPairs: ParamPair[_]*): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  111. final def setDefault[T](param: Param[T], value: T): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  112. def setIncludeAllConfidenceScores(value: Boolean): MedicalNerModel.this.type

    whether to include confidence scores for all tags rather than just for the predicted one

  113. def setIncludeConfidence(value: Boolean): MedicalNerModel.this.type

    whether to include confidence scores in annotation metadata

  114. def setInferenceBatchSize(value: Int): MedicalNerModel.this.type

    set the number of sentences to process in a single batch during inference

  115. final def setInputCols(value: String*): MedicalNerModel.this.type
    Definition Classes
    HasInputAnnotationCols
  116. final def setInputCols(value: Array[String]): MedicalNerModel.this.type
    Definition Classes
    HasInputAnnotationCols
  117. def setLazyAnnotator(value: Boolean): MedicalNerModel.this.type
    Definition Classes
    CanBeLazy
  118. def setMinProbability(minProba: Float): MedicalNerModel.this.type

    Minimum probability.

    Minimum probability. Used only if there is no CRF on top of LSTM layer.

  119. def setModelIfNotSet(spark: SparkSession, tf: TensorflowWrapper): MedicalNerModel.this.type
  120. final def setOutputCol(value: String): MedicalNerModel.this.type
    Definition Classes
    HasOutputAnnotationCol
  121. def setParent(parent: Estimator[MedicalNerModel]): MedicalNerModel
    Definition Classes
    Model
  122. def setStorageRef(value: String): MedicalNerModel.this.type
    Definition Classes
    HasStorageRef
  123. val storageRef: Param[String]
    Definition Classes
    HasStorageRef
  124. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  125. def tag(tokenized: Array[Array[WordpieceEmbeddingsSentence]]): Seq[Array[NerTaggedSentence]]
  126. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  127. final def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    AnnotatorModel → Transformer
  128. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  129. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  130. final def transformSchema(schema: StructType): StructType
    Definition Classes
    RawAnnotator → PipelineStage
  131. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  132. val uid: String
    Definition Classes
    MedicalNerModel → Identifiable
  133. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  134. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  135. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  136. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  137. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  138. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  139. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  140. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
    Definition Classes
    WriteTensorflowModel
  141. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
    Definition Classes
    WriteTensorflowModel
  142. 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 Licensed

Inherited from HasStorageRef

Inherited from WriteTensorflowModel

Inherited from HasBatchedAnnotate[MedicalNerModel]

Inherited from AnnotatorModel[MedicalNerModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[MedicalNerModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[MedicalNerModel]

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

anno

getParam

param

setParam

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