class MedicalNerModel extends AnnotatorModel[MedicalNerModel] with HasBatchedAnnotate[MedicalNerModel] with WriteTensorflowModel with HasStorageRef with ParamsAndFeaturesWritable with CheckLicense
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- MedicalNerModel
- CheckLicense
- HasStorageRef
- WriteTensorflowModel
- HasBatchedAnnotate
- AnnotatorModel
- CanBeLazy
- RawAnnotator
- HasOutputAnnotationCol
- HasInputAnnotationCols
- HasOutputAnnotatorType
- ParamsAndFeaturesWritable
- HasFeatures
- DefaultParamsWritable
- MLWritable
- Model
- Transformer
- PipelineStage
- Logging
- Params
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final
def
!=(arg0: Any): Boolean
- Definition Classes
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final
def
##(): Int
- Definition Classes
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final
def
$[T](param: Param[T]): T
- Attributes
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- Definition Classes
- Params
-
def
$$[T](feature: StructFeature[T]): T
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
$$[K, V](feature: MapFeature[K, V]): Map[K, V]
- Attributes
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- Definition Classes
- HasFeatures
-
def
$$[T](feature: SetFeature[T]): Set[T]
- Attributes
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- Definition Classes
- HasFeatures
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def
$$[T](feature: ArrayFeature[T]): Array[T]
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final
def
==(arg0: Any): Boolean
- Definition Classes
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-
def
_transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
def
afterAnnotate(dataset: DataFrame): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
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def
batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]
- Definition Classes
- MedicalNerModel → HasBatchedAnnotate
-
def
batchProcess(rows: Iterator[_]): Iterator[Row]
- Definition Classes
- HasBatchedAnnotate
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val
batchSize: IntParam
- Definition Classes
- HasBatchedAnnotate
-
def
beforeAnnotate(dataset: Dataset[_]): Dataset[_]
- Attributes
- protected
- Definition Classes
- MedicalNerModel → AnnotatorModel
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final
def
checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
def
checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
- Definition Classes
- CheckLicense
-
def
checkValidScope(scope: String): Unit
- Definition Classes
- CheckLicense
-
def
checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
- Definition Classes
- CheckLicense
-
def
checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
- Definition Classes
- CheckLicense
- val classes: StringArrayParam
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final
def
clear(param: Param[_]): MedicalNerModel.this.type
- Definition Classes
- Params
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
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val
configProtoBytes: IntArrayParam
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
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def
copy(extra: ParamMap): MedicalNerModel
- Definition Classes
- RawAnnotator → Model → Transformer → PipelineStage → Params
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
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def
createDatabaseConnection(database: Name): RocksDBConnection
- Definition Classes
- HasStorageRef
-
val
datasetParams: StructFeature[DatasetEncoderParams]
datasetParams
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
-
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.
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
explainParam(param: Param[_]): String
- Definition Classes
- Params
-
def
explainParams(): String
- Definition Classes
- Params
-
def
extraValidate(structType: StructType): Boolean
- Attributes
- protected
- Definition Classes
- RawAnnotator
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def
extraValidateMsg: String
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
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final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
-
val
features: ArrayBuffer[Feature[_, _, _]]
- Definition Classes
- HasFeatures
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
get[T](feature: StructFeature[T]): Option[T]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[T](feature: SetFeature[T]): Option[Set[T]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
get[T](feature: ArrayFeature[T]): Option[Array[T]]
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getBatchSize: Int
- Definition Classes
- HasBatchedAnnotate
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
getClasses: Array[String]
get the tags used to trained this MedicalNerModel
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def
getConfigProtoBytes: Option[Array[Byte]]
datasetParams
- def getConfigProtoBytesAsInt: Option[Array[Int]]
- def getDatasetParams: DatasetEncoderParams
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final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getIncludeAllConfidenceScores: Boolean
whether to include all confidence scores in annotation metadata or just the score of the predicted tag
-
def
getIncludeConfidence: Boolean
whether to include confidence scores in annotation metadata
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def
getInferenceBatchSize: Int
get the number of sentences to process in a single batch during inference
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def
getInputCols: Array[String]
- Definition Classes
- HasInputAnnotationCols
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def
getLazyAnnotator: Boolean
- Definition Classes
- CanBeLazy
-
def
getLicenseScopes: Seq[String]
- Attributes
- protected
-
def
getMinProba: Float
Minimum probability.
Minimum probability. Used only if there is no CRF on top of LSTM layer.
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def
getModelIfNotSet: TensorflowMedicalNer
ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
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final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
- Params
-
final
def
getOutputCol: String
- Definition Classes
- HasOutputAnnotationCol
-
def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
-
def
getSentenceTokenIndex: Boolean
whether to include the token index for each sentence in annotation metadata
-
def
getStorageRef: String
- Definition Classes
- HasStorageRef
- def getTrainingClassDistribution: Map[String, Long]
- def getTrainingClassDistributionJava: Map[String, Long]
-
final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
-
def
hasParent: Boolean
- Definition Classes
- Model
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
val
includeAllConfidenceScores: BooleanParam
whether to include all confidence scores in annotation metadata or just score of the predicted tag
-
val
includeConfidence: BooleanParam
whether to include confidence scores in annotation metadata
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val
inferenceBatchSize: IntParam
Number of sentences to process in a single batch during inference
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
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
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final
val
inputCols: StringArrayParam
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
isDefined(param: Param[_]): Boolean
- Definition Classes
- Params
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
isSet(param: Param[_]): Boolean
- Definition Classes
- Params
-
def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
-
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
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val
lazyAnnotator: BooleanParam
- Definition Classes
- CanBeLazy
-
def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logDebug(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logError(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logInfo(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logName: String
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
-
val
minProba: FloatParam
Minimum probability.
Minimum probability. Used only if there is no CRF on top of LSTM layer.
-
def
msgHelper(schema: StructType): String
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
onWrite(path: String, spark: SparkSession): Unit
- Definition Classes
- MedicalNerModel → ParamsAndFeaturesWritable
-
val
optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
-
val
outputAnnotatorType: String
Output Annnotator type : NAMED_ENTITY
Output Annnotator type : NAMED_ENTITY
- Definition Classes
- MedicalNerModel → HasOutputAnnotatorType
-
final
val
outputCol: Param[String]
- Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
-
lazy val
params: Array[Param[_]]
- Definition Classes
- Params
-
var
parent: Estimator[MedicalNerModel]
- Definition Classes
- Model
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
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.
-
def
set[T](feature: StructFeature[T], value: T): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: SetFeature[T], value: Set[T]): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: ArrayFeature[T], value: Array[T]): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
set(paramPair: ParamPair[_]): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): MedicalNerModel.this.type
- Definition Classes
- Params
-
def
setBatchSize(size: Int): MedicalNerModel.this.type
- Definition Classes
- HasBatchedAnnotate
-
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()
-
def
setDatasetParams(params: DatasetEncoderParams): MedicalNerModel.this.type
datasetParams
-
def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
setDefault(paramPairs: ParamPair[_]*): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): MedicalNerModel.this.type
- Attributes
- protected
- Definition Classes
- Params
-
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.
-
def
setIncludeAllConfidenceScores(value: Boolean): MedicalNerModel.this.type
whether to include confidence scores for all tags rather than just for the predicted one
-
def
setIncludeConfidence(value: Boolean): MedicalNerModel.this.type
whether to include confidence scores in annotation metadata
-
def
setInferenceBatchSize(value: Int): MedicalNerModel.this.type
set the number of sentences to process in a single batch during inference
-
final
def
setInputCols(value: String*): MedicalNerModel.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setInputCols(value: Array[String]): MedicalNerModel.this.type
- Definition Classes
- HasInputAnnotationCols
- def setLabelCasing(value: String): MedicalNerModel.this.type
-
def
setLazyAnnotator(value: Boolean): MedicalNerModel.this.type
- Definition Classes
- CanBeLazy
-
def
setMinProbability(minProba: Float): MedicalNerModel.this.type
Minimum probability.
Minimum probability. Used only if there is no CRF on top of LSTM layer.
- def setModelIfNotSet(spark: SparkSession, tf: TensorflowWrapper): MedicalNerModel.this.type
-
final
def
setOutputCol(value: String): MedicalNerModel.this.type
- Definition Classes
- HasOutputAnnotationCol
-
def
setParent(parent: Estimator[MedicalNerModel]): MedicalNerModel
- Definition Classes
- Model
-
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.
-
def
setStorageRef(value: String): MedicalNerModel.this.type
- Definition Classes
- HasStorageRef
-
def
setTrainingClassDistribution(value: Map[String, Long]): MedicalNerModel.this.type
set the number of sentences to process in a single batch during inference
-
val
storageRef: Param[String]
- Definition Classes
- HasStorageRef
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
- def tag(tokenized: Array[Array[WordpieceEmbeddingsSentence]]): Seq[Array[NerTaggedSentence]]
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
- val trainingClassDistribution: MapFeature[String, Long]
-
final
def
transform(dataset: Dataset[_]): DataFrame
- Definition Classes
- AnnotatorModel → Transformer
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
final
def
transformSchema(schema: StructType): StructType
- Definition Classes
- RawAnnotator → PipelineStage
-
def
transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
-
val
uid: String
- Definition Classes
- MedicalNerModel → Identifiable
-
def
validate(schema: StructType): Boolean
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
- Definition Classes
- HasStorageRef
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
def
wrapColumnMetadata(col: Column): Column
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
def
write: MLWriter
- Definition Classes
- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
-
def
writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String): Unit
- Definition Classes
- WriteTensorflowModel
-
def
writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]]): Unit
- Definition Classes
- WriteTensorflowModel
-
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