com.johnsnowlabs.nlp.annotators.classification
MedicalBertForTokenClassifier
Companion object MedicalBertForTokenClassifier
class MedicalBertForTokenClassifier extends AnnotatorModel[MedicalBertForTokenClassifier] with HasBatchedAnnotate[MedicalBertForTokenClassifier] with WriteTensorflowModel with WriteOnnxModel with HasCaseSensitiveProperties with HasEngine with CheckLicense
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- MedicalBertForTokenClassifier
- CheckLicense
- HasEngine
- HasCaseSensitiveProperties
- WriteOnnxModel
- WriteTensorflowModel
- HasBatchedAnnotate
- AnnotatorModel
- CanBeLazy
- RawAnnotator
- HasOutputAnnotationCol
- HasInputAnnotationCols
- HasOutputAnnotatorType
- ParamsAndFeaturesWritable
- HasFeatures
- DefaultParamsWritable
- MLWritable
- Model
- Transformer
- PipelineStage
- Logging
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final
def
!=(arg0: Any): Boolean
- Definition Classes
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final
def
##(): Int
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final
def
$[T](param: Param[T]): T
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def
$$[T](feature: StructFeature[T]): T
- Attributes
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- HasFeatures
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def
$$[K, V](feature: MapFeature[K, V]): Map[K, V]
- Attributes
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- HasFeatures
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def
$$[T](feature: SetFeature[T]): Set[T]
- Attributes
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- HasFeatures
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def
$$[T](feature: ArrayFeature[T]): Array[T]
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final
def
==(arg0: Any): Boolean
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def
_transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
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def
afterAnnotate(dataset: DataFrame): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
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def
batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]
takes a document and annotations and produces new annotations of this annotator's annotation type
takes a document and annotations and produces new annotations of this annotator's annotation type
- batchedAnnotations
Annotations that correspond to inputAnnotationCols generated by previous annotators if any
- returns
any number of annotations processed for every input annotation. Not necessary one to one relationship
- Definition Classes
- MedicalBertForTokenClassifier → HasBatchedAnnotate
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def
batchProcess(rows: Iterator[_]): Iterator[Row]
- Definition Classes
- HasBatchedAnnotate
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val
batchSize: IntParam
- Definition Classes
- HasBatchedAnnotate
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def
beforeAnnotate(dataset: Dataset[_]): Dataset[_]
- Attributes
- protected
- Definition Classes
- MedicalBertForTokenClassifier → AnnotatorModel
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val
caseSensitive: BooleanParam
- Definition Classes
- HasCaseSensitiveProperties
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final
def
checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
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def
checkValidEnvironment(spark: Option[SparkSession], scopes: Seq[String]): Unit
- Definition Classes
- CheckLicense
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def
checkValidScope(scope: String): Unit
- Definition Classes
- CheckLicense
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def
checkValidScopeAndEnvironment(scope: String, spark: Option[SparkSession], checkLp: Boolean): Unit
- Definition Classes
- CheckLicense
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def
checkValidScopesAndEnvironment(scopes: Seq[String], spark: Option[SparkSession], checkLp: Boolean): Unit
- Definition Classes
- CheckLicense
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final
def
clear(param: Param[_]): MedicalBertForTokenClassifier.this.type
- Definition Classes
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def
clone(): AnyRef
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- protected[lang]
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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): MedicalBertForTokenClassifier
- Definition Classes
- RawAnnotator → Model → Transformer → PipelineStage → Params
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def
copyValues[T <: Params](to: T, extra: ParamMap): T
- Attributes
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- Definition Classes
- Params
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final
def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
- protected
- Definition Classes
- Params
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val
engine: Param[String]
- Definition Classes
- HasEngine
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
equals(arg0: Any): Boolean
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def
explainParam(param: Param[_]): String
- Definition Classes
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def
explainParams(): String
- Definition Classes
- Params
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def
extraValidate(structType: StructType): Boolean
- Attributes
- protected
- Definition Classes
- RawAnnotator
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def
extraValidateMsg: String
- Attributes
- protected
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- RawAnnotator
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final
def
extractParamMap(): ParamMap
- Definition Classes
- Params
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final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
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val
features: ArrayBuffer[Feature[_, _, _]]
- Definition Classes
- HasFeatures
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def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
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- @throws( classOf[java.lang.Throwable] )
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def
get[T](feature: StructFeature[T]): Option[T]
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
get[T](feature: SetFeature[T]): Option[Set[T]]
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
get[T](feature: ArrayFeature[T]): Option[Array[T]]
- Attributes
- protected
- Definition Classes
- HasFeatures
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final
def
get[T](param: Param[T]): Option[T]
- Definition Classes
- Params
-
def
getBatchSize: Int
- Definition Classes
- HasBatchedAnnotate
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def
getCaseSensitive: Boolean
- Definition Classes
- HasCaseSensitiveProperties
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final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
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def
getClasses: Array[String]
Returns labels used to train this model
- def getConfigProtoBytes: Option[Array[Byte]]
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final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
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def
getEngine: String
- Definition Classes
- HasEngine
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def
getInputCols: Array[String]
- Definition Classes
- HasInputAnnotationCols
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def
getLabels: Map[String, Int]
Returns the Labels parameter
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def
getLazyAnnotator: Boolean
- Definition Classes
- CanBeLazy
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def
getMaxSentenceLength: Int
Returns the maxSentenceLength parameter
- def getModelIfNotSet: MedicalBertClassification
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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
getSignatures: Option[Map[String, String]]
Returns the signatures parameter
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final
def
hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
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def
hasParam(paramName: String): Boolean
- Definition Classes
- Params
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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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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
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val
inputAnnotatorTypes: Array[String]
Input Annotator Types: DOCUMENT, TOKEN
Input Annotator Types: DOCUMENT, TOKEN
- Definition Classes
- MedicalBertForTokenClassifier → HasInputAnnotationCols
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final
val
inputCols: StringArrayParam
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
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final
def
isDefined(param: Param[_]): Boolean
- Definition Classes
- Params
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final
def
isInstanceOf[T0]: Boolean
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final
def
isSet(param: Param[_]): Boolean
- Definition Classes
- Params
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def
isTraceEnabled(): Boolean
- Attributes
- protected
- Definition Classes
- Logging
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val
labels: MapFeature[String, Int]
Labels used to decode predicted IDs back to string tags
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val
lazyAnnotator: BooleanParam
- Definition Classes
- CanBeLazy
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def
log: Logger
- Attributes
- protected
- Definition Classes
- Logging
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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- Definition Classes
- Logging
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def
logDebug(msg: ⇒ String): Unit
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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def
logError(msg: ⇒ String): Unit
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def
logInfo(msg: ⇒ String, throwable: Throwable): Unit
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- Definition Classes
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def
logInfo(msg: ⇒ String): Unit
- Attributes
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- Definition Classes
- Logging
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def
logName: String
- Attributes
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- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
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- Definition Classes
- Logging
-
def
logTrace(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logWarning(msg: ⇒ String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
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def
logWarning(msg: ⇒ String): Unit
- Attributes
- protected
- Definition Classes
- Logging
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val
maxSentenceLength: IntParam
Max sentence length to process (Default:
128
) -
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()
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
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def
onWrite(path: String, spark: SparkSession): Unit
- Definition Classes
- MedicalBertForTokenClassifier → ParamsAndFeaturesWritable
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val
optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
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val
outputAnnotatorType: AnnotatorType
Output Annotator Types: WORD_EMBEDDINGS
Output Annotator Types: WORD_EMBEDDINGS
- Definition Classes
- MedicalBertForTokenClassifier → 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[MedicalBertForTokenClassifier]
- Definition Classes
- Model
-
def
save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
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def
sentenceEndTokenId: Int
Returns the end of sentence token ("[SEP]") associated id.
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def
sentenceStartTokenId: Int
Returns the start of sentence token ("[CLS]") associated id.
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def
set[T](feature: StructFeature[T], value: T): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: SetFeature[T], value: Set[T]): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
set[T](feature: ArrayFeature[T], value: Array[T]): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
set(paramPair: ParamPair[_]): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set(param: String, value: Any): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
set[T](param: Param[T], value: T): MedicalBertForTokenClassifier.this.type
- Definition Classes
- Params
-
def
setBatchSize(size: Int): MedicalBertForTokenClassifier.this.type
- Definition Classes
- HasBatchedAnnotate
-
def
setCaseSensitive(value: Boolean): MedicalBertForTokenClassifier.this.type
- Definition Classes
- HasCaseSensitiveProperties
-
def
setConfigProtoBytes(bytes: Array[Int]): MedicalBertForTokenClassifier.this.type
Sets the configProtoBytes parameter
-
def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
-
final
def
setDefault(paramPairs: ParamPair[_]*): MedicalBertForTokenClassifier.this.type
- Attributes
- protected
- Definition Classes
- Params
-
final
def
setDefault[T](param: Param[T], value: T): MedicalBertForTokenClassifier.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
-
final
def
setInputCols(value: String*): MedicalBertForTokenClassifier.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setInputCols(value: Array[String]): MedicalBertForTokenClassifier.this.type
- Definition Classes
- HasInputAnnotationCols
-
def
setLabels(value: Map[String, Int]): MedicalBertForTokenClassifier.this.type
Sets the labels parameter
-
def
setLazyAnnotator(value: Boolean): MedicalBertForTokenClassifier.this.type
- Definition Classes
- CanBeLazy
-
def
setMaxSentenceLength(value: Int): MedicalBertForTokenClassifier.this.type
Sets the maxSentenceLength parameter
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def
setModelIfNotSet(spark: SparkSession, onnxWrapper: OnnxWrapper): MedicalBertForTokenClassifier.this.type
Sets the model if it is not set yet
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def
setModelIfNotSet(spark: SparkSession, tensorflowWrapper: TensorflowWrapper): MedicalBertForTokenClassifier.this.type
Sets the model if it is not set yet
-
final
def
setOutputCol(value: String): MedicalBertForTokenClassifier.this.type
- Definition Classes
- HasOutputAnnotationCol
-
def
setParent(parent: Estimator[MedicalBertForTokenClassifier]): MedicalBertForTokenClassifier
- Definition Classes
- Model
-
def
setSignatures(value: Map[String, String]): MedicalBertForTokenClassifier.this.type
Sets the signatures parameter
-
def
setVocabulary(value: Map[String, Int]): MedicalBertForTokenClassifier.this.type
Sets the vocabulary parameter
-
val
signatures: MapFeature[String, String]
It contains TF model signatures for the laded saved model
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
-
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
- MedicalBertForTokenClassifier → Identifiable
-
def
validate(schema: StructType): Boolean
- Attributes
- protected
- Definition Classes
- RawAnnotator
-
val
vocabulary: MapFeature[String, Int]
Vocabulary used to encode the words to ids with WordPieceEncoder
-
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: OnnxWrapper, suffix: String, fileName: String): Unit
- Definition Classes
- WriteOnnxModel
-
def
writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
- Definition Classes
- WriteOnnxModel
-
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