class MedicalSummarizer extends MedicalEncoderDecoder
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        _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
      
      
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        afterAnnotate(dataset: DataFrame): DataFrame
      
      
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        def
      
      
        batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]
      
      
      - Definition Classes
- MedicalEncoderDecoder → HasBatchedAnnotate
 
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        def
      
      
        batchProcess(rows: Iterator[_]): Iterator[Row]
      
      
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        batchSize: IntParam
      
      
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        beforeAnnotate(dataset: Dataset[_]): Dataset[_]
      
      
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        val
      
      
        caseSensitive: BooleanParam
      
      
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        checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
      
      
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- HasInputAnnotationCols
 
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        def
      
      
        checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
      
      
      - Definition Classes
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        def
      
      
        checkValidScope(scope: String): Unit
      
      
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        checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
      
      
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        checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
      
      
      - Definition Classes
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        final 
        def
      
      
        clear(param: Param[_]): MedicalSummarizer.this.type
      
      
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        clone(): AnyRef
      
      
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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() - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        copy(extra: ParamMap): MedicalEncoderDecoder
      
      
      - Definition Classes
- RawAnnotator → Model → Transformer → PipelineStage → Params
 
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        copyValues[T <: Params](to: T, extra: ParamMap): T
      
      
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        defaultCopy[T <: Params](extra: ParamMap): T
      
      
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        val
      
      
        doSample: BooleanParam
      
      
      Whether or not to use sampling, use greedy decoding otherwise (Default: false)Whether or not to use sampling, use greedy decoding otherwise (Default: false)- Definition Classes
- MedicalEncoderDecoder
 
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        val
      
      
        engine: Param[String]
      
      
      - Definition Classes
- HasEngine
 
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        eq(arg0: AnyRef): Boolean
      
      
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        explainParam(param: Param[_]): String
      
      
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        explainParams(): String
      
      
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        extraValidate(structType: StructType): Boolean
      
      
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        extraValidateMsg: String
      
      
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        extractParamMap(): ParamMap
      
      
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        extractParamMap(extra: ParamMap): ParamMap
      
      
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        features: ArrayBuffer[Feature[_, _, _]]
      
      
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        finalize(): Unit
      
      
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        get[T](feature: ArrayFeature[T]): Option[Array[T]]
      
      
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        get[T](param: Param[T]): Option[T]
      
      
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        getBatchSize: Int
      
      
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        getCaseSensitive: Boolean
      
      
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        getClass(): Class[_]
      
      
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        def
      
      
        getConfigProtoBytes: Option[Array[Byte]]
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        final 
        def
      
      
        getDefault[T](param: Param[T]): Option[T]
      
      
      - Definition Classes
- Params
 
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        def
      
      
        getDoSample: Boolean
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getEngine: String
      
      
      - Definition Classes
- HasEngine
 
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        def
      
      
        getIgnoreTokenIds: Array[Int]
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getInputCols: Array[String]
      
      
      - Definition Classes
- HasInputAnnotationCols
 
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        def
      
      
        getLazyAnnotator: Boolean
      
      
      - Definition Classes
- CanBeLazy
 
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        def
      
      
        getMaxNewTokens: Int
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getMaxTextLength: Int
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        getMlFrameworkType: String
      
      
      Get ML framework type Get ML framework type - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getModelIfNotSet: MedicalEncoderDecoderModel
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getNoRepeatNgramSize: Int
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        final 
        def
      
      
        getOrDefault[T](param: Param[T]): T
      
      
      - Definition Classes
- Params
 
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        final 
        def
      
      
        getOutputCol: String
      
      
      - Definition Classes
- HasOutputAnnotationCol
 
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        def
      
      
        getParam(paramName: String): Param[Any]
      
      
      - Definition Classes
- Params
 
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        def
      
      
        getRandomSeed: Option[Int]
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getRefineChunkSize: Int
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        getRefineMaxAttempts: Int
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getRefineSummary: Boolean
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getRefineSummaryTargetLength: Int
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getSignatures: Option[Map[String, String]]
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getStopAtEos: Boolean
      
      
      Checks whether text generation stops when the end-of-sentence token is encountered. Checks whether text generation stops when the end-of-sentence token is encountered. - Definition Classes
- MedicalSummarizer → MedicalEncoderDecoder
 
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        def
      
      
        getTask: String
      
      
      - Attributes
- protected
- Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getTopK: Int
      
      
      - Attributes
- protected
- Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        getUseCache: Boolean
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
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        final 
        def
      
      
        hasDefault[T](param: Param[T]): Boolean
      
      
      - Definition Classes
- Params
 
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        hasParam(paramName: String): Boolean
      
      
      - Definition Classes
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        def
      
      
        hasParent: Boolean
      
      
      - Definition Classes
- Model
 
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        def
      
      
        hashCode(): Int
      
      
      - Definition Classes
- AnyRef → Any
- Annotations
- @native()
 
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        var
      
      
        ignoreTokenIds: IntArrayParam
      
      
      A list of token ids which are ignored in the decoder's output (Default: Array())A list of token ids which are ignored in the decoder's output (Default: Array())- Definition Classes
- MedicalEncoderDecoder
 
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        def
      
      
        initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
      
      
      - Attributes
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        def
      
      
        initializeLogIfNecessary(isInterpreter: Boolean): Unit
      
      
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- Logging
 
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        val
      
      
        inputAnnotatorTypes: Array[AnnotatorType]
      
      
      Input annotator type : DOCUMENT Input annotator type : DOCUMENT - Definition Classes
- MedicalEncoderDecoder → HasInputAnnotationCols
 
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        final 
        val
      
      
        inputCols: StringArrayParam
      
      
      - Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
 
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        def
      
      
        isDefined(param: Param[_]): Boolean
      
      
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        isInstanceOf[T0]: Boolean
      
      
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        isSet(param: Param[_]): Boolean
      
      
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        isTraceEnabled(): Boolean
      
      
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        lazyAnnotator: BooleanParam
      
      
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        log: Logger
      
      
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        logInfo(msg: ⇒ String, throwable: Throwable): Unit
      
      
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        def
      
      
        logName: String
      
      
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        logTrace(msg: ⇒ String, throwable: Throwable): Unit
      
      
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        logTrace(msg: ⇒ String): Unit
      
      
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        def
      
      
        logWarning(msg: ⇒ String, throwable: Throwable): Unit
      
      
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        logWarning(msg: ⇒ String): Unit
      
      
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        val
      
      
        maxNewTokens: IntParam
      
      
      Maximum number of new tokens to be generated (Default: 30) Maximum number of new tokens to be generated (Default: 30) - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        maxTextLength: IntParam
      
      
      Maximum length of context text. Maximum length of context text. (Default: 1024)- Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        mlFrameworkType: Param[String]
      
      
      ML framework type ML framework type - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        msgHelper(schema: StructType): String
      
      
      - Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
 
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        final 
        def
      
      
        ne(arg0: AnyRef): Boolean
      
      
      - Definition Classes
- AnyRef
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        noRepeatNgramSize: IntParam
      
      
      If set to int > 0, all ngrams of that size can only occur once (Default:0)If set to int > 0, all ngrams of that size can only occur once (Default:0)- Definition Classes
- MedicalEncoderDecoder
 
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        final 
        def
      
      
        notify(): Unit
      
      
      - Definition Classes
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        final 
        def
      
      
        notifyAll(): Unit
      
      
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- @native()
 
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        def
      
      
        onWrite(path: String, spark: SparkSession): Unit
      
      
      - Definition Classes
- MedicalSummarizer → MedicalEncoderDecoder → ParamsAndFeaturesWritable
 
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        def
      
      
        onWriteSkip(path: String, spark: SparkSession): Unit
      
      
      - Attributes
- protected
- Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        optionalInputAnnotatorTypes: Array[String]
      
      
      - Definition Classes
- HasInputAnnotationCols
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        outputAnnotatorType: String
      
      
      Output annotator type : DOCUMENT Output annotator type : DOCUMENT - Definition Classes
- MedicalEncoderDecoder → HasOutputAnnotatorType
 
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        final 
        val
      
      
        outputCol: Param[String]
      
      
      - Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
 
- 
      
      
      
        
      
    
      
        
        lazy val
      
      
        params: Array[Param[_]]
      
      
      - Definition Classes
- Params
 
- 
      
      
      
        
      
    
      
        
        var
      
      
        parent: Estimator[MedicalEncoderDecoder]
      
      
      - Definition Classes
- Model
 
- 
      
      
      
        
      
    
      
        
        var
      
      
        randomSeed: Option[Int]
      
      
      Optional Random seed for the model. Optional Random seed for the model. Needs to be of type Long.- Definition Classes
- MedicalEncoderDecoder
 
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        val
      
      
        refineChunkSize: IntParam
      
      
      How large should refined chunks Be. How large should refined chunks Be. Should be equal to LLM context window size in tokens. Takes only effect when refineSummary=True - Definition Classes
- MedicalEncoderDecoder
 
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        val
      
      
        refineMaxAttempts: IntParam
      
      
      How many times should chunks be re-summarized while they are above SummaryTargetLength before stopping. How many times should chunks be re-summarized while they are above SummaryTargetLength before stopping. Takes only effect when refineSummary=True - Definition Classes
- MedicalEncoderDecoder
 
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        val
      
      
        refineSummary: BooleanParam
      
      
      Set true to perform refined summarization at increased computation cost. Set true to perform refined summarization at increased computation cost. - Definition Classes
- MedicalEncoderDecoder
 
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        val
      
      
        refineSummaryTargetLength: IntParam
      
      
      Target length for refined summary. Target length for refined summary. Text will be chunked and re-summarized until it is below this length or maximum attempts are used up. - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        save(path: String): Unit
      
      
      - Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        set[T](feature: StructFeature[T], value: T): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- HasFeatures
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- HasFeatures
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        set[T](feature: SetFeature[T], value: Set[T]): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- HasFeatures
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        set[T](feature: ArrayFeature[T], value: Array[T]): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- HasFeatures
 
- 
      
      
      
        
      
    
      
        final 
        def
      
      
        set(paramPair: ParamPair[_]): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- Params
 
- 
      
      
      
        
      
    
      
        final 
        def
      
      
        set(param: String, value: Any): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- Params
 
- 
      
      
      
        
      
    
      
        final 
        def
      
      
        set[T](param: Param[T], value: T): MedicalSummarizer.this.type
      
      
      - Definition Classes
- Params
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setBatchSize(size: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- HasBatchedAnnotate
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setCaseSensitive(value: Boolean): MedicalSummarizer.this.type
      
      
      - Definition Classes
- HasCaseSensitiveProperties
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setConfigProtoBytes(bytes: Array[Int]): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setDefault[T](feature: StructFeature[T], value: () ⇒ T): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- HasFeatures
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- HasFeatures
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- HasFeatures
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- HasFeatures
 
- 
      
      
      
        
      
    
      
        final 
        def
      
      
        setDefault(paramPairs: ParamPair[_]*): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- Params
 
- 
      
      
      
        
      
    
      
        final 
        def
      
      
        setDefault[T](param: Param[T], value: T): MedicalSummarizer.this.type
      
      
      - Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setDoSample(value: Boolean): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setIgnoreTokenIds(tokenIds: Array[Int]): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        final 
        def
      
      
        setInputCols(value: String*): MedicalSummarizer.this.type
      
      
      - Definition Classes
- HasInputAnnotationCols
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setInputCols(value: Array[String]): MedicalSummarizer.this.type
      
      
      - Definition Classes
- HasInputAnnotationCols
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setLazyAnnotator(value: Boolean): MedicalSummarizer.this.type
      
      
      - Definition Classes
- CanBeLazy
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setMaxNewTokens(value: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setMaxTextLength(value: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setMlFrameworkType(value: String): MedicalSummarizer.this.type
      
      
      Set ML framework type Set ML framework type - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setModelIfNotSet(spark: SparkSession, encoder: InternalOnnxWrapper, decoder: InternalOnnxWrapper, spp: SentencePieceWrapper): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setModelIfNotSet(spark: SparkSession, tfWrapper: TensorflowWrapper, spp: SentencePieceWrapper, useCache: Boolean): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setNoRepeatNgramSize(value: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        final 
        def
      
      
        setOutputCol(value: String): MedicalSummarizer.this.type
      
      
      - Definition Classes
- HasOutputAnnotationCol
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setParent(parent: Estimator[MedicalEncoderDecoder]): MedicalEncoderDecoder
      
      
      - Definition Classes
- Model
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setRandomSeed(value: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setRefineChunkSize(value: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setRefineMaxAttempts(value: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setRefineSummary(value: Boolean): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setRefineSummaryTargetLength(value: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setSignatures(value: Map[String, String]): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setStopAtEos(value: Boolean): MedicalSummarizer.this.type
      
      
      Determines whether text generation stops when the end-of-sentence token is encountered. Determines whether text generation stops when the end-of-sentence token is encountered. - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setTask(value: String): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setTopK(value: Int): MedicalSummarizer.this.type
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        setUseCache(value: Boolean): MedicalSummarizer.this.type
      
      
      - Attributes
- protected
- Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        signatures: MapFeature[String, String]
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        stopAtEos: BooleanParam
      
      
      - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        final 
        def
      
      
        synchronized[T0](arg0: ⇒ T0): T0
      
      
      - Definition Classes
- AnyRef
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        task: Param[String]
      
      
      Set transformer task, e.g. Set transformer task, e.g. "summarize:"(Default:""). The T5 task needs to be in the format"task:".- Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        toString(): String
      
      
      - Definition Classes
- Identifiable → AnyRef → Any
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        topK: IntParam
      
      
      The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)- Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        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
- MedicalSummarizer → MedicalEncoderDecoder → Identifiable
 
- 
      
      
      
        
      
    
      
        
        val
      
      
        useCache: BooleanParam
      
      
      Cache internal state of the model to improve performance Cache internal state of the model to improve performance - Definition Classes
- MedicalEncoderDecoder
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        validate(schema: StructType): Boolean
      
      
      - Attributes
- protected
- Definition Classes
- RawAnnotator
 
- 
      
      
      
        
      
    
      
        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
      
      
        writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: InternalOnnxWrapper, suffix: String, fileName: String, encrypt: Boolean): Unit
      
      
      - Definition Classes
- InternalWriteOnnxModel
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: InternalOnnxWrapper, suffix: String, fileName: String): Unit
      
      
      - Definition Classes
- InternalWriteOnnxModel
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(InternalOnnxWrapper, String)], suffix: String, encrypt: Boolean = false): Unit
      
      
      - Definition Classes
- InternalWriteOnnxModel
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(InternalOnnxWrapper, String)], suffix: String): Unit
      
      
      - Definition Classes
- InternalWriteOnnxModel
 
- 
      
      
      
        
      
    
      
        
        def
      
      
        writeSentencePieceModel(path: String, spark: SparkSession, spp: SentencePieceWrapper, suffix: String, filename: String): Unit
      
      
      - Definition Classes
- WriteSentencePieceModel
 
- 
      
      
      
        
      
    
      
        
        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