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

Linear Supertypes
CheckLicense, 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. CheckLicense
  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. def checkValidEnvironment(spark: Option[SparkSession]): Unit
    Definition Classes
    CheckLicense
  16. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  17. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  18. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  19. final def clear(param: Param[_]): MedicalNerApproach.this.type
    Definition Classes
    Params
  20. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  21. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

  22. final def copy(extra: ParamMap): Estimator[MedicalNerModel]
    Definition Classes
    AnnotatorApproach → Estimator → PipelineStage → Params
  23. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. val description: String

    Trains Tensorflow based Char-CNN-BLSTM model

    Trains Tensorflow based Char-CNN-BLSTM model

    Definition Classes
    MedicalNerApproach → AnnotatorApproach
  26. val dropout: FloatParam

    "Dropout coefficient

  27. val earlyStoppingCriterion: FloatParam

    If set, this param specifies the criterion to stop training if performance is not improving.

    If set, this param specifies the criterion to stop training if performance is not improving. Default value is 0 which is means that early stopping is not used.

  28. val earlyStoppingPatience: IntParam

    Number of epochs with no performance improvement before training is terminated.

    Number of epochs with no performance improvement before training is terminated. Default value is 0.

  29. val enableMemoryOptimizer: BooleanParam
  30. val enableOutputLogs: BooleanParam

    Whether to output to annotators log folder

  31. val entities: StringArrayParam
    Definition Classes
    NerApproach
  32. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  33. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  34. 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.

  35. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  36. def explainParams(): String
    Definition Classes
    Params
  37. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  38. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  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. final def fit(dataset: Dataset[_]): MedicalNerModel
    Definition Classes
    AnnotatorApproach → Estimator
  42. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[MedicalNerModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  43. def fit(dataset: Dataset[_], paramMap: ParamMap): MedicalNerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  44. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): MedicalNerModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  45. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  46. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  47. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  48. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  49. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  50. def getBatchSize: Int

    Batch size

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

    ConfigProto from tensorflow, serialized into byte array.

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

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

    Dropout coefficient

  55. def getEarlyStoppingCriterion: Float

    Early stopping criterion

  56. def getEarlyStoppingPatience: Int

    Early stopping patience

  57. def getEnableMemoryOptimizer: Boolean

    Memory Optimizer

  58. def getEnableOutputLogs: Boolean

    Whether to output to annotators log folder

  59. def getIncludeAllConfidenceScores: Boolean

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

  60. def getIncludeConfidence: Boolean

    whether to include confidence scores in annotation metadata

  61. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  62. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  63. def getLogName: String
    Definition Classes
    MedicalNerApproach → Logging
  64. def getLr: Float

    Learning Rate

  65. def getMaxEpochs: Int
    Definition Classes
    NerApproach
  66. def getMinEpochs: Int
    Definition Classes
    NerApproach
  67. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  68. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  69. def getOutputLogsPath: String
  70. def getOverrideExistingTags: Boolean

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

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

    Learning rate decay coefficient.

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

  73. def getRandomSeed: Int
    Definition Classes
    NerApproach
  74. def getUseBestModel: Boolean

    useBestModel

  75. def getUseContrib: Boolean

    Whether to use contrib LSTM Cells.

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

  76. 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.

  77. def getVerbose: Int
    Definition Classes
    NerApproach
  78. val graphFile: Param[String]

    File path that contain external graph file

  79. val graphFolder: Param[String]

    Folder path that contain external graph files

  80. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  81. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  82. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  83. val includeAllConfidenceScores: BooleanParam

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

  84. val includeConfidence: BooleanParam

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

  85. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  86. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. val inputAnnotatorTypes: Array[String]

    Input annotator types : DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Input annotator types : DOCUMENT, TOKEN, WORD_EMBEDDINGS

    Definition Classes
    MedicalNerApproach → HasInputAnnotationCols
  88. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  89. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  90. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  91. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  92. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  93. val labelColumn: Param[String]
    Definition Classes
    NerApproach
  94. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  95. def log(value: ⇒ String, minLevel: Level): Unit
    Attributes
    protected
    Definition Classes
    Logging
  96. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  97. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  98. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  99. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  100. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  101. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  102. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  103. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  104. val logPrefix: Param[String]

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

  105. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  106. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  107. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  108. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  109. val logger: Logger
    Attributes
    protected
    Definition Classes
    Logging
  110. val lr: FloatParam

    Learning Rate

  111. val maxEpochs: IntParam
    Definition Classes
    NerApproach
  112. val minEpochs: IntParam
    Definition Classes
    NerApproach
  113. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  114. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  115. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  116. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  117. def onTrained(model: MedicalNerModel, spark: SparkSession): Unit
    Definition Classes
    AnnotatorApproach
  118. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  119. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  120. val outputAnnotatorType: String

    Input annotator types : NAMED_ENTITY

    Input annotator types : NAMED_ENTITY

    Definition Classes
    MedicalNerApproach → HasOutputAnnotatorType
  121. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  122. def outputLog(value: ⇒ String, uuid: String, shouldLog: Boolean, outputLogsPath: String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  123. val outputLogsPath: Param[String]
  124. 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.

  125. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  126. val po: FloatParam

    Learning rate decay coefficient.

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

  127. val pretrainedModelPath: Param[String]

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

  128. val randomSeed: IntParam
    Definition Classes
    NerApproach
  129. def resumeTrainingFromModel(model: MedicalNerModel): MedicalNerApproach.this.type
  130. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  131. def set[T](feature: StructFeature[T], value: T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  132. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  133. def set[T](feature: SetFeature[T], value: Set[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  134. def set[T](feature: ArrayFeature[T], value: Array[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  135. final def set(paramPair: ParamPair[_]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  136. final def set(param: String, value: Any): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  137. final def set[T](param: Param[T], value: T): MedicalNerApproach.this.type
    Definition Classes
    Params
  138. def setBatchSize(batch: Int): MedicalNerApproach.this.type

    Batch size

  139. 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()

  140. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  141. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  142. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  143. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  144. final def setDefault(paramPairs: ParamPair[_]*): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  145. final def setDefault[T](param: Param[T], value: T): MedicalNerApproach.this.type
    Attributes
    protected
    Definition Classes
    Params
  146. def setDropout(dropout: Float): MedicalNerApproach.this.type

    Dropout coefficient

  147. def setEarlyStoppingCriterion(value: Float): MedicalNerApproach.this.type

  148. def setEarlyStoppingPatience(value: Int): MedicalNerApproach.this.type

  149. def setEnableMemoryOptimizer(value: Boolean): MedicalNerApproach.this.type
  150. def setEnableOutputLogs(enableOutputLogs: Boolean): MedicalNerApproach.this.type

    Whether to output to annotators log folder

  151. def setEntities(tags: Array[String]): MedicalNerApproach
    Definition Classes
    NerApproach
  152. 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.

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

    Folder path that contain external graph files

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

    Folder path that contain external graph files

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

    Whether to include confidence scores in annotation metadata

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

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

  157. final def setInputCols(value: String*): MedicalNerApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  158. def setInputCols(value: Array[String]): MedicalNerApproach.this.type
    Definition Classes
    HasInputAnnotationCols
  159. def setLabelColumn(column: String): MedicalNerApproach
    Definition Classes
    NerApproach
  160. def setLazyAnnotator(value: Boolean): MedicalNerApproach.this.type
    Definition Classes
    CanBeLazy
  161. def setLogPrefix(value: String): MedicalNerApproach.this.type

    a string prefix to be included in the logs

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

    Learning Rate

  163. def setMaxEpochs(epochs: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  164. def setMinEpochs(epochs: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  165. final def setOutputCol(value: String): MedicalNerApproach.this.type
    Definition Classes
    HasOutputAnnotationCol
  166. def setOutputLogsPath(path: String): MedicalNerApproach.this.type
  167. 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.

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

    Learning rate decay coefficient.

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

  169. 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.

  170. def setRandomSeed(seed: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  171. 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.

  172. 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.

  173. 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.

  174. 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.

  175. def setUseBestModel(value: Boolean): MedicalNerApproach.this.type

  176. 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.

  177. 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.

  178. def setVerbose(verbose: Level): MedicalNerApproach
    Definition Classes
    NerApproach
  179. def setVerbose(verbose: Int): MedicalNerApproach
    Definition Classes
    NerApproach
  180. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  181. 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.

  182. 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.")

  183. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  184. def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): MedicalNerModel
    Definition Classes
    MedicalNerApproach → AnnotatorApproach
  185. final def transformSchema(schema: StructType): StructType
    Definition Classes
    AnnotatorApproach → PipelineStage
  186. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  187. val uid: String
    Definition Classes
    MedicalNerApproach → Identifiable
  188. val useBestModel: BooleanParam

    Whether to restore and use the model that has achieved the best performance at the end of the training.

    Whether to restore and use the model that has achieved the best performance at the end of the training. The metric that is being monitored is macro F1 for testDataset and if it's not set it will be validationSplit, and if it's not set finally looks for loss.

  189. val useContrib: BooleanParam

    whether to use contrib LSTM Cells.

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

  190. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    AnnotatorApproach
  191. 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.

  192. val verbose: IntParam
    Definition Classes
    NerApproach
  193. val verboseLevel: Level
    Definition Classes
    MedicalNerApproach → Logging
  194. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  195. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  196. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  197. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from CheckLicense

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