class MedicalNerApproach extends AnnotatorApproach[MedicalNerModel] with NerApproach[MedicalNerApproach] with Logging with ParamsAndFeaturesWritable with Licensed

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Licensed, ParamsAndFeaturesWritable, HasFeatures, Logging, NerApproach[MedicalNerApproach], AnnotatorApproach[MedicalNerModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[MedicalNerModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. MedicalNerApproach
  2. Licensed
  3. ParamsAndFeaturesWritable
  4. HasFeatures
  5. Logging
  6. NerApproach
  7. AnnotatorApproach
  8. CanBeLazy
  9. DefaultParamsWritable
  10. MLWritable
  11. HasOutputAnnotatorType
  12. HasOutputAnnotationCol
  13. HasInputAnnotationCols
  14. Estimator
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

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. 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 _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): MedicalNerModel
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  10. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  11. val batchSize: IntParam

    Batch size

  12. def beforeTraining(spark: SparkSession): Unit
    Definition Classes
    MedicalNerApproach → AnnotatorApproach
  13. def calculateEmbeddingsDim(sentences: Seq[WordpieceEmbeddingsSentence]): Int
  14. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  15. final def clear(param: Param[_]): MedicalNerApproach.this.type
    Definition Classes
    Params
  16. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  17. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

  18. final def copy(extra: ParamMap): Estimator[MedicalNerModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  19. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. val description: String

    Trains Tensorflow based Char-CNN-BLSTM model

    Trains Tensorflow based Char-CNN-BLSTM model

    Definition Classes
    MedicalNerApproach → AnnotatorApproach
  22. val dropout: FloatParam

    "Dropout coefficient

  23. val enableMemoryOptimizer: BooleanParam
  24. val enableOutputLogs: BooleanParam

    Whether to output to annotators log folder

  25. val entities: StringArrayParam
    Definition Classes
    NerApproach
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. val evaluationLogExtended: BooleanParam

    Whether logs for validation to be extended: it displays time and evaluation of each label.

    Whether logs for validation to be extended: it displays time and evaluation of each label. Default is false.

  29. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  30. def explainParams(): String
    Definition Classes
    Params
  31. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  32. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  33. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  34. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  35. final def fit(dataset: Dataset[_]): MedicalNerModel
    Definition Classes
    AnnotatorApproach → Estimator
  36. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[MedicalNerModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  37. def fit(dataset: Dataset[_], paramMap: ParamMap): MedicalNerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  38. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): MedicalNerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  39. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  41. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  42. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  43. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  44. def getBatchSize: Int

    Batch size

  45. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  46. def getConfigProtoBytes: Option[Array[Byte]]

    ConfigProto from tensorflow, serialized into byte array.

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

  47. def getDataSetParams(dsIt: Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]]): (Set[String], Set[Char], Int, Long)
  48. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  49. def getDropout: Float

    Dropout coefficient

  50. def getEnableMemoryOptimizer: Boolean

    Memory Optimizer

  51. def getEnableOutputLogs: Boolean

    Whether to output to annotators log folder

  52. def getIncludeAllConfidenceScores: Boolean

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

  53. def getIncludeConfidence: Boolean

    whether to include confidence scores in annotation metadata

  54. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  55. def getIteratorFunc(dataset: Dataset[Row]): () ⇒ Iterator[Array[(TextSentenceLabels, WordpieceEmbeddingsSentence)]]
  56. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  57. def getLogName: String
    Definition Classes
    MedicalNerApproach → Logging
  58. def getLr: Float

    Learning Rate

  59. def getMaxEpochs: Int
    Definition Classes
    NerApproach
  60. def getMinEpochs: Int
    Definition Classes
    NerApproach
  61. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  62. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  63. def getOutputLogsPath: String
  64. def getOverrideExistingTags: Boolean

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

  65. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  66. def getPo: Float

    Learning rate decay coefficient.

    Learning rate decay coefficient. Real Learning Rage = lr / (1 + po * epoch)

  67. def getRandomSeed: Int
    Definition Classes
    NerApproach
  68. def getUseContrib: Boolean

    Whether to use contrib LSTM Cells.

    Whether to use contrib LSTM Cells. Not compatible with Windows. Might slightly improve accuracy.

  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.

  70. def getVerbose: Int
    Definition Classes
    NerApproach
  71. val graphFile: Param[String]

    File path that contain external graph file

  72. val graphFolder: Param[String]

    Folder path that contain external graph files

  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. val includeAllConfidenceScores: BooleanParam

    val includeAllConfidenceScores = new BooleanParam(this, "includeAllConfidenceScores", "whether to include all confidence scores in annotation metadata")

  77. val includeConfidence: BooleanParam

    val includeConfidence = new BooleanParam(this, "includeConfidence", "Whether to include confidence scores in annotation metadata")

  78. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  79. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. val inputAnnotatorTypes: Array[String]

    Input annotator types : DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Input annotator types : DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Definition Classes
    MedicalNerApproach → HasInputAnnotationCols
  81. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  82. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  83. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  84. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  85. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  86. val labelColumn: Param[String]
    Definition Classes
    NerApproach
  87. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  88. def log(value: ⇒ String, minLevel: Level): Unit
    Attributes
    protected
    Definition Classes
    Logging
  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. val logPrefix: Param[String]

    A prefix that will be appended to every log, default value is empty.

  98. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  99. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  100. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  101. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  102. val logger: Logger
    Attributes
    protected
    Definition Classes
    Logging
  103. val lr: FloatParam

    Learning Rate

  104. val maxEpochs: IntParam
    Definition Classes
    NerApproach
  105. val minEpochs: IntParam
    Definition Classes
    NerApproach
  106. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  107. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  108. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  109. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  110. def onTrained(model: MedicalNerModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  111. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  112. val outputAnnotatorType: String

    Input annotator types : NAMED_ENTITY

    Input annotator types : NAMED_ENTITY

    Definition Classes
    MedicalNerApproach → HasOutputAnnotatorType
  113. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  114. def outputLog(value: ⇒ String, uuid: String, shouldLog: Boolean, outputLogsPath: String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  115. val outputLogsPath: Param[String]
  116. val overrideExistingTags: BooleanParam

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

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

  117. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  118. val po: FloatParam

    Learning rate decay coefficient.

    Learning rate decay coefficient. Real Learning Rage = lr / (1 + po * epoch)

  119. val pretrainedModelPath: Param[String]

    Path to an already trained MedicalNerModel, which is used as a starting point for training the new model.

  120. val randomSeed: IntParam
    Definition Classes
    NerApproach
  121. def resumeTrainingFromModel(model: MedicalNerModel): MedicalNerApproach.this.type
  122. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  123. def set[T](feature: StructFeature[T], value: T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  124. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  125. def set[T](feature: SetFeature[T], value: Set[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  126. def set[T](feature: ArrayFeature[T], value: Array[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  127. final def set(paramPair: ParamPair[_]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  128. final def set(param: String, value: Any): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  129. final def set[T](param: Param[T], value: T): MedicalNerApproach.this.type
    Definition Classes
    Params
  130. def setBatchSize(batch: Int): MedicalNerApproach.this.type

    Batch size

  131. def setConfigProtoBytes(bytes: Array[Int]): MedicalNerApproach.this.type

    ConfigProto from tensorflow, serialized into byte array.

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

  132. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  133. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  134. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  135. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  136. final def setDefault(paramPairs: ParamPair[_]*): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  137. final def setDefault[T](param: Param[T], value: T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  138. def setDropout(dropout: Float): MedicalNerApproach.this.type

    Dropout coefficient

  139. def setEnableMemoryOptimizer(value: Boolean): MedicalNerApproach.this.type
  140. def setEnableOutputLogs(enableOutputLogs: Boolean): MedicalNerApproach.this.type

    Whether to output to annotators log folder

  141. def setEntities(tags: Array[String]): MedicalNerApproach
    Definition Classes
    NerApproach
  142. def setEvaluationLogExtended(evaluationLogExtended: Boolean): MedicalNerApproach.this.type

    Whether logs for validation to be extended: it displays time and evaluation of each label.

    Whether logs for validation to be extended: it displays time and evaluation of each label. Default is false.

  143. def setGraphFile(path: String): MedicalNerApproach.this.type

    Folder path that contain external graph files

  144. def setGraphFolder(path: String): MedicalNerApproach.this.type

    Folder path that contain external graph files

  145. def setIncludeAllConfidenceScores(value: Boolean): MedicalNerApproach.this.type

    Whether to include confidence scores in annotation metadata

  146. def setIncludeConfidence(value: Boolean): MedicalNerApproach.this.type

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

  147. final def setInputCols(value: String*): MedicalNerApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  148. final def setInputCols(value: Array[String]): MedicalNerApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  149. def setLabelColumn(column: String): MedicalNerApproach
    Definition Classes
    NerApproach
  150. def setLazyAnnotator(value: Boolean): MedicalNerApproach.this.type
    Definition Classes
    CanBeLazy
  151. def setLogPrefix(value: String): MedicalNerApproach.this.type

    a string prefix to be included in the logs

  152. def setLr(lr: Float): MedicalNerApproach.this.type

    Learning Rate

  153. def setMaxEpochs(epochs: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  154. def setMinEpochs(epochs: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  155. final def setOutputCol(value: String): MedicalNerApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  156. def setOutputLogsPath(path: String): MedicalNerApproach.this.type
  157. def setOverrideExistingTags(value: Boolean): MedicalNerApproach.this.type

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

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

  158. def setPo(po: Float): MedicalNerApproach.this.type

    Learning rate decay coefficient.

    Learning rate decay coefficient. Real Learning Rage = lr / (1 + po * epoch)

  159. def setPretrainedModelPath(path: String): MedicalNerApproach.this.type

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

  160. def setRandomSeed(seed: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  161. def setTagsMapping(mapping: Map[String, String]): MedicalNerApproach.this.type

    A map specifying how old tags are mapped to new ones.

    A map specifying how old tags are mapped to new ones. Maps are specified either using a list of comma separated strings, e.g. ("OLDTAG1,NEWTAG1", "OLDTAG2,NEWTAG2", ...) or by a Map data structure.

  162. def setTagsMapping(mapping: Array[String]): MedicalNerApproach.this.type

    A map specifying how old tags are mapped to new ones.

    A map specifying how old tags are mapped to new ones. Maps are specified either using a list of comma separated strings, e.g. ("OLDTAG1,NEWTAG1", "OLDTAG2,NEWTAG2", ...) or by a Map data structure. It only works if setOverrideExistingTags is false.

  163. def setTestDataset(er: ExternalResource): MedicalNerApproach.this.type

    Path to test dataset.

    Path to test dataset. If set used to calculate statistic on it during training.

  164. def setTestDataset(path: String, readAs: Format = ReadAs.SPARK, options: Map[String, String] = Map("format" -> "parquet")): MedicalNerApproach.this.type

    Path to test dataset.

    Path to test dataset. If set used to calculate statistic on it during training.

  165. def setUseContrib(value: Boolean): MedicalNerApproach.this.type

    Whether to use contrib LSTM Cells.

    Whether to use contrib LSTM Cells. Not compatible with Windows. Might slightly improve accuracy.

  166. def setValidationSplit(validationSplit: Float): MedicalNerApproach.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.

  167. def setVerbose(verbose: Level): MedicalNerApproach
    Definition Classes
    NerApproach
  168. def setVerbose(verbose: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  169. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  170. val tagsMapping: MapFeature[String, String]

    A map specifying how old tags are mapped to new ones.

    A map specifying how old tags are mapped to new ones. It only works if setOverrideExistingTags is false.

  171. val testDataset: ExternalResourceParam

    val testDataset = new ExternalResourceParam(this, "testDataset", "Path to test dataset.

    val testDataset = new ExternalResourceParam(this, "testDataset", "Path to test dataset. If set used to calculate statistic on it during training.")

  172. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  173. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): MedicalNerModel
    Definition Classes
    MedicalNerApproach → AnnotatorApproach
  174. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  175. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  176. val uid: String
    Definition Classes
    MedicalNerApproach → Identifiable
  177. val useContrib: BooleanParam

    whether to use contrib LSTM Cells.

    whether to use contrib LSTM Cells. Not compatible with Windows. Might slightly improve accuracy.

  178. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  179. val validationSplit: FloatParam

    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.

  180. val verbose: IntParam
    Definition Classes
    NerApproach
  181. val verboseLevel: Level
    Definition Classes
    MedicalNerApproach → Logging
  182. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  183. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  184. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  185. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from Licensed

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from Logging

Inherited from NerApproach[MedicalNerApproach]

Inherited from AnnotatorApproach[MedicalNerModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[MedicalNerModel]

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

Ungrouped