class MedicalNerModel extends AnnotatorModel[MedicalNerModel] with MedicalNerParams with HasBatchedAnnotate[MedicalNerModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable with CheckLicense

Linear Supertypes
CheckLicense, HasStorageRef, WriteTensorflowModel, HasBatchedAnnotate[MedicalNerModel], MedicalNerParams, AnnotatorModel[MedicalNerModel], CanBeLazy, RawAnnotator[MedicalNerModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[MedicalNerModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
FinanceNerModel, LegalNerModel
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. MedicalNerModel
  2. CheckLicense
  3. HasStorageRef
  4. WriteTensorflowModel
  5. HasBatchedAnnotate
  6. MedicalNerParams
  7. AnnotatorModel
  8. CanBeLazy
  9. RawAnnotator
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. HasOutputAnnotatorType
  13. ParamsAndFeaturesWritable
  14. HasFeatures
  15. DefaultParamsWritable
  16. MLWritable
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. 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. def checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
    Definition Classes
    CheckLicense
  18. def checkValidScope(scope: String): Unit
    Definition Classes
    CheckLicense
  19. def checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  20. def checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
    Definition Classes
    CheckLicense
  21. val classes: StringArrayParam
  22. final def clear(param: Param[_]): MedicalNerModel.this.type
    Definition Classes
    Params
  23. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  24. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

    Definition Classes
    MedicalNerParams
  25. def copy(extra: ParamMap): MedicalNerModel
    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  26. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  27. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  28. val datasetInfo: Param[String]

    Descriptive information about the dataset being used.

    Descriptive information about the dataset being used.

    Definition Classes
    MedicalNerParams
  29. val datasetParams: StructFeature[DatasetEncoderParams]

    datasetParams

  30. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  31. val doExceptionHandling: BooleanParam

    If true, effective batchsize is 1 and exceptions are handled.

    If true, effective batchsize is 1 and 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 batch. This comes with a performance penalty.

  32. val dropout: FloatParam

    Dropout coefficient, by default 0.5.

    Dropout coefficient, by default 0.5.

    The coefficient of the dropout layer. The value should be between 0.0 and 1.0. Internally, it is used by Tensorflow as: rate = 1.0 - dropout when adding a dropout layer on top of the recurrent layers.

    Definition Classes
    MedicalNerParams
  33. 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.

    The criterion is set to F1-score if the validationSplit is greater than 0.0 (F1-socre on validation set) or testDataset is defined (F1-score on test set), otherwise it is set to model loss. The priority is as follows: - If testDataset is defined, then the criterion is set to F1-score on test set. - If validationSplit is greater than 0.0, then the criterion is set to F1-score on validation set. - Otherwise, the criterion is set to model loss.

    Note that while the F1-score ranges from 0.0 to 1.0, the loss ranges from 0.0 to infinity. So, depending on which case you are in, the value you use for the criterion can be very different. For example, if validationSplit is 0.1, then a criterion of 0.01 means that if the F1-score on the validation set difference from last epoch is greater than 0.01, then the training should stop. However, if there is not validation or test set defined, then a criterion of 2.0 means that if the loss difference between the last epoch and the current one is less than 2.0, then training should stop.

    Definition Classes
    MedicalNerParams
    See also

    earlyStoppingPatience.

  34. val earlyStoppingPatience: IntParam

    Number of epochs to wait before early stopping if no improvement, by default 5.

    Number of epochs to wait before early stopping if no improvement, by default 5.

    Given the earlyStoppingCriterion, if the performance does not improve for the given number of epochs, then the training will stop. If the value is 0, then early stopping will occurs as soon as the criterion is met (no patience).

    Definition Classes
    MedicalNerParams
    See also

    earlyStoppingCriterion.

  35. val enableMemoryOptimizer: BooleanParam

    Whether to optimize for large datasets or not.

    Whether to optimize for large datasets or not. Enabling this option can slow down training.

    In practice, if set to true the training will iterate over the spark Data Frame and retrieve the batches from the Data Frame iterator. This can be slower than the default option as it has to collect the batches on evey bach for every epoch, but it can be useful if the dataset is too large to fit in memory.

    It controls if we want the features collected and generated at once and then feed into the network batch by batch (False) or collected and generated by batch and then feed into the network in batches (True) .

    If the training data can fit to memory, then it is recommended to set this option to False (default value).

    Definition Classes
    MedicalNerParams
  36. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  37. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  38. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  39. def explainParams(): String
    Definition Classes
    Params
  40. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  41. def extraValidateMsg: String
    Attributes
    protected
    Definition Classes
    RawAnnotator
  42. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  43. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  44. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  45. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  46. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  47. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  48. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  49. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  50. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  51. def getBatchSize: Int
    Definition Classes
    HasBatchedAnnotate
  52. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  53. def getClasses: Array[String]

    get the tags used to trained this MedicalNerModel

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

    ConfigProto from tensorflow, serialized into byte array.

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

    Definition Classes
    MedicalNerParams
  55. def getConfigProtoBytesAsInt: Option[Array[Int]]
  56. def getDatasetInfo: String

    get descriptive information about the dataset being used

    get descriptive information about the dataset being used

    Definition Classes
    MedicalNerParams
  57. def getDatasetParams: DatasetEncoderParams
  58. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  59. def getDropout: Float

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    MedicalNerParams
  60. def getEarlyStoppingCriterion: Float

    Early stopping criterion

    Early stopping criterion

    Definition Classes
    MedicalNerParams
  61. def getEarlyStoppingPatience: Int

    Early stopping patience

    Early stopping patience

    Definition Classes
    MedicalNerParams
  62. def getEnableMemoryOptimizer: Boolean

    Whether to optimize for large datasets or not.

    Whether to optimize for large datasets or not. Enabling this option can slow down training.

    Definition Classes
    MedicalNerParams
  63. def getIncludeAllConfidenceScores: Boolean

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

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

    Definition Classes
    MedicalNerParams
  64. def getIncludeConfidence: Boolean

    whether to include confidence scores in annotation metadata

    whether to include confidence scores in annotation metadata

    Definition Classes
    MedicalNerParams
  65. def getInferenceBatchSize: Int

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

  66. def getInputCols: Array[String]
    Definition Classes
    HasInputAnnotationCols
  67. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  68. def getLicenseScopes: Seq[String]
    Attributes
    protected
  69. def getLr: Float

    Learning Rate

    Learning Rate

    Definition Classes
    MedicalNerParams
  70. def getMinProba: Float

    Minimum probability.

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

  71. def getModelIfNotSet: TensorflowMedicalNer

    ConfigProto from tensorflow, serialized into byte array.

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

  72. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  73. final def getOutputCol: String
    Definition Classes
    HasOutputAnnotationCol
  74. def getOverrideExistingTags: Boolean

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

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

    Definition Classes
    MedicalNerParams
  75. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  76. def getPo: Float

    Learning rate decay coefficient.

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

    Definition Classes
    MedicalNerParams
  77. def getRandomValidationSplitPerEpoch: Boolean

    Checks if a random validation split is done after each epoch or at the beginning of training only.

    Checks if a random validation split is done after each epoch or at the beginning of training only.

    Definition Classes
    MedicalNerParams
  78. def getSentenceTokenIndex: Boolean

    whether to include the token index for each sentence in annotation metadata.

    whether to include the token index for each sentence in annotation metadata.

    Definition Classes
    MedicalNerParams
  79. def getStorageRef: String
    Definition Classes
    HasStorageRef
  80. def getTrainingClassDistribution: Map[String, Long]
  81. def getTrainingClassDistributionJava: Map[String, Long]
  82. def getUseBestModel: Boolean

    useBestModel

    useBestModel

    Definition Classes
    MedicalNerParams
  83. def getUseContrib: Boolean

    Whether to use contrib LSTM Cells.

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

    Definition Classes
    MedicalNerParams
  84. val graphFile: Param[String]

    Path that contains the external graph file.

    Path that contains the external graph file.

    When specified, the provided file will be used, and no graph search will happen. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

    Definition Classes
    MedicalNerParams
  85. val graphFolder: Param[String]

    Folder path that contains external graph files.

    Folder path that contains external graph files.

    The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

    When instantiating the Tensorflow model, uses this folder to search for the adequate Tensorflow graph. The search is done using the name of the .pb file, which should be in this format: blstn_{ntags}_{embedding_dim}_{lstm_size}_{nchars}.pb.

    Then, the search follows these rules: - Embedding dimension should be exactly the same as the one used to train the model. - Number of unique tags should be greater than or equal to the number of unique tags in the training data. - Number of unique chars should be greater than or equal to the number of unique chars in the training data.

    The returned file will be the first one that satisfies all the conditions.

    If the name of the file is ill-formed, errors will occur during training.

    Definition Classes
    MedicalNerParams
  86. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  87. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  88. def hasParent: Boolean
    Definition Classes
    Model
  89. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  90. val includeAllConfidenceScores: BooleanParam

    Whether to include confidence scores for all tags in annotation metadata or just the score of the predicted tag, by default False.

    Whether to include confidence scores for all tags in annotation metadata or just the score of the predicted tag, by default False.

    Needs the includeConfidence parameter to be set to true.

    Enabling this may slow down the inference speed.

    Definition Classes
    MedicalNerParams
  91. val includeConfidence: BooleanParam

    Whether to include confidence scores in annotation metadata, by default False.

    Whether to include confidence scores in annotation metadata, by default False.

    Setting this parameter to True will add the confidence score to the metadata of the NAMED_ENTITY annotation. In addition, if includeAllConfidenceScores is set to true, then the confidence scores of all the tags will be added to the metadata, otherwise only for the predicted tag (the one with maximum score).

    Definition Classes
    MedicalNerParams
  92. val inferenceBatchSize: IntParam

    Number of sentences to process in a single batch during inference

  93. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  94. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  95. 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
  96. final val inputCols: StringArrayParam
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  97. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  98. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  99. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  100. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  101. val labelCasing: Param[String]

    Set the tag to case sensitive or not.Setting all labels of the NER models upper/lower case.

    Set the tag to case sensitive or not.Setting all labels of the NER models upper/lower case. values upper|lower

  102. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  103. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  104. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  105. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  106. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  107. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  108. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  109. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  110. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  111. val logPrefix: Param[String]

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

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

    Definition Classes
    MedicalNerParams
  112. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  113. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  114. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  115. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  116. val lr: FloatParam

    Learning Rate, by default 0.001.

    Learning Rate, by default 0.001.

    Definition Classes
    MedicalNerParams
  117. val minProba: FloatParam

    Minimum probability.

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

  118. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  119. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  120. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  121. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  122. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    MedicalNerModel → ParamsAndFeaturesWritable
  123. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  124. val outputAnnotatorType: String

    Output Annnotator type : NAMED_ENTITY

    Output Annnotator type : NAMED_ENTITY

    Definition Classes
    MedicalNerModel → HasOutputAnnotatorType
  125. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  126. 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.

    Definition Classes
    MedicalNerParams
  127. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  128. var parent: Estimator[MedicalNerModel]
    Definition Classes
    Model
  129. val po: FloatParam

    Learning rate decay coefficient (time-based).

    Learning rate decay coefficient (time-based).

    This is used to calculate the decayed learning rate at each step as: lr = lr / (1 + po * epoch), meaning that the value of the learning rate is updated on each epoch. By default 0.005.

    Definition Classes
    MedicalNerParams
  130. val pretrainedModelPath: Param[String]

    Path to an already trained MedicalNerModel.

    Path to an already trained MedicalNerModel.

    This pretrained model will be used as a starting point for training the new one. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

    Definition Classes
    MedicalNerParams
  131. val randomValidationSplitPerEpoch: BooleanParam

    Do a random validation split after each epoch rather than at the beginning of training only.

    Do a random validation split after each epoch rather than at the beginning of training only.

    Definition Classes
    MedicalNerParams
  132. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  133. val sentenceTokenIndex: BooleanParam

    whether to include the token index for each sentence in annotation metadata, by default false.

    whether to include the token index for each sentence in annotation metadata, by default false. If the value is true, the process might be slowed down.

    Definition Classes
    MedicalNerParams
  134. def set[T](feature: StructFeature[T], value: T): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  135. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  136. def set[T](feature: SetFeature[T], value: Set[T]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  137. def set[T](feature: ArrayFeature[T], value: Array[T]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  138. final def set(paramPair: ParamPair[_]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  139. final def set(param: String, value: Any): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  140. final def set[T](param: Param[T], value: T): MedicalNerModel.this.type
    Definition Classes
    Params
  141. def setBatchSize(size: Int): MedicalNerModel.this.type
    Definition Classes
    HasBatchedAnnotate
  142. 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()

    Definition Classes
    MedicalNerParams
  143. def setDatasetInfo(value: String): MedicalNerModel.this.type

    set descriptive information about the dataset being used

    set descriptive information about the dataset being used

    Definition Classes
    MedicalNerParams
  144. def setDatasetParams(params: DatasetEncoderParams): MedicalNerModel.this.type

    datasetParams

  145. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  146. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  147. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  148. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  149. final def setDefault(paramPairs: ParamPair[_]*): MedicalNerModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  150. final def setDefault[T](param: Param[T], value: T): MedicalNerModel.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  151. def setDoExceptionHandling(value: Boolean): MedicalNerModel.this.type

    If true, effective batchsize is 1 and exceptions are handled.

    If true, effective batchsize is 1 and 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 batch. This comes with a performance penalty.

  152. def setDropout(dropout: Float): MedicalNerModel.this.type

    Dropout coefficient

    Dropout coefficient

    Definition Classes
    MedicalNerParams
  153. def setEarlyStoppingCriterion(value: Float): MedicalNerModel.this.type

    Definition Classes
    MedicalNerParams
  154. def setEarlyStoppingPatience(value: Int): MedicalNerModel.this.type

    Definition Classes
    MedicalNerParams
  155. def setEnableMemoryOptimizer(value: Boolean): MedicalNerModel.this.type
    Definition Classes
    MedicalNerParams
  156. def setGraphFile(path: String): MedicalNerModel.this.type

    Folder path that contain external graph files

    Folder path that contain external graph files

    Definition Classes
    MedicalNerParams
  157. def setGraphFolder(path: String): MedicalNerModel.this.type

    Folder path that contain external graph files

    Folder path that contain external graph files

    Definition Classes
    MedicalNerParams
  158. def setIncludeAllConfidenceScores(value: Boolean): MedicalNerModel.this.type

    Whether to include confidence scores in annotation metadata

    Whether to include confidence scores in annotation metadata

    Definition Classes
    MedicalNerParams
  159. def setIncludeConfidence(value: Boolean): MedicalNerModel.this.type

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

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

    Definition Classes
    MedicalNerParams
  160. def setInferenceBatchSize(value: Int): MedicalNerModel.this.type

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

  161. final def setInputCols(value: String*): MedicalNerModel.this.type
    Definition Classes
    HasInputAnnotationCols
  162. def setInputCols(value: Array[String]): MedicalNerModel.this.type
    Definition Classes
    HasInputAnnotationCols
  163. def setLabelCasing(value: String): MedicalNerModel.this.type
  164. def setLazyAnnotator(value: Boolean): MedicalNerModel.this.type
    Definition Classes
    CanBeLazy
  165. def setLogPrefix(value: String): MedicalNerModel.this.type

    a string prefix to be included in the logs

    a string prefix to be included in the logs

    Definition Classes
    MedicalNerParams
  166. def setLr(lr: Float): MedicalNerModel.this.type

    Learning Rate

    Learning Rate

    Definition Classes
    MedicalNerParams
  167. def setMinProbability(minProba: Float): MedicalNerModel.this.type

    Minimum probability.

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

  168. def setModelIfNotSet(spark: SparkSession, tf: TensorflowWrapper): MedicalNerModel.this.type
  169. final def setOutputCol(value: String): MedicalNerModel.this.type
    Definition Classes
    HasOutputAnnotationCol
  170. def setOverrideExistingTags(value: Boolean): MedicalNerModel.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.

    Definition Classes
    MedicalNerParams
  171. def setParent(parent: Estimator[MedicalNerModel]): MedicalNerModel
    Definition Classes
    Model
  172. def setPo(po: Float): MedicalNerModel.this.type

    Learning rate decay coefficient.

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

    Definition Classes
    MedicalNerParams
  173. def setPretrainedModelPath(path: String): MedicalNerModel.this.type

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

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

    Definition Classes
    MedicalNerParams
  174. def setRandomValidationSplitPerEpoch(value: Boolean): MedicalNerModel.this.type

    Do a random validation split after each epoch rather than at the beginning of training only.

    Do a random validation split after each epoch rather than at the beginning of training only.

    Definition Classes
    MedicalNerParams
  175. def setSentenceTokenIndex(value: Boolean): MedicalNerModel.this.type

    whether to include the token index for each sentence in annotation metadata, by default false.

    whether to include the token index for each sentence in annotation metadata, by default false. If the value is true, the process might be slowed down.

    Definition Classes
    MedicalNerParams
  176. def setStorageRef(value: String): MedicalNerModel.this.type
    Definition Classes
    HasStorageRef
  177. def setTagsMapping(mapping: Map[String, String]): MedicalNerModel.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.

    Definition Classes
    MedicalNerParams
  178. def setTagsMapping(mapping: ArrayList[String]): MedicalNerModel.this.type
    Definition Classes
    MedicalNerParams
  179. def setTagsMapping(mapping: Array[String]): MedicalNerModel.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.

    Definition Classes
    MedicalNerParams
  180. def setTrainingClassDistribution(value: Map[String, Long]): MedicalNerModel.this.type

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

  181. def setUseBestModel(value: Boolean): MedicalNerModel.this.type

    Definition Classes
    MedicalNerParams
  182. def setUseContrib(value: Boolean): MedicalNerModel.this.type

    Whether to use contrib LSTM Cells.

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

    Definition Classes
    MedicalNerParams
  183. val storageRef: Param[String]
    Definition Classes
    HasStorageRef
  184. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  185. def tag(tokenized: Array[Array[WordpieceEmbeddingsSentence]]): Seq[Array[NerTaggedSentence]]
  186. 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 overrideExistingTags is set to false.

    Definition Classes
    MedicalNerParams
  187. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  188. val trainingClassDistribution: MapFeature[String, Long]
  189. final def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    AnnotatorModel → Transformer
  190. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  191. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  192. final def transformSchema(schema: StructType): StructType
    Definition Classes
    RawAnnotator → PipelineStage
  193. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  194. val uid: String
    Definition Classes
    MedicalNerModel → Identifiable
  195. val useBestModel: BooleanParam

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

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

    By default false (keep the model from the last trained epoch).

    The best model depends on the earlyStoppingCriterion, which can be F1-score on test/validation dataset or the value of loss.

    Definition Classes
    MedicalNerParams
  196. val useContrib: BooleanParam

    whether to use contrib LSTM Cells.

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

    Definition Classes
    MedicalNerParams
  197. def validate(schema: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  198. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  199. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  200. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  201. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  202. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  203. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  204. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
    Definition Classes
    WriteTensorflowModel
  205. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
    Definition Classes
    WriteTensorflowModel
  206. 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 CheckLicense

Inherited from HasStorageRef

Inherited from WriteTensorflowModel

Inherited from HasBatchedAnnotate[MedicalNerModel]

Inherited from MedicalNerParams

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

Ungrouped