SentenceDetectorDLApproach

Instance Constructors

new SentenceDetectorDLApproach()
new SentenceDetectorDLApproach(uid: String)

Type Members

type AnnotatorType = String

Definition Classes
HasOutputAnnotatorType

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): SentenceDetectorDLModel

Attributes
protected
Definition Classes
AnnotatorApproach
final def asInstanceOf[T0]: T0

Definition Classes
Any
def beforeTraining(spark: SparkSession): Unit

Definition Classes
AnnotatorApproach
final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

Attributes
protected
Definition Classes
HasInputAnnotationCols
final def clear(param: Param[_]): SentenceDetectorDLApproach.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def copy(extra: ParamMap): Estimator[SentenceDetectorDLModel]

Definition Classes
AnnotatorApproach → Estimator → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
val description: String

Trains TensorFlow model for multi-class text classification
Trains TensorFlow model for multi-class text classification

Definition Classes
SentenceDetectorDLApproach → AnnotatorApproach
val epochsNumber: IntParam

Maximum number of epochs to train
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 explodeSentences: BooleanParam

A flag indicating whether to split sentences into different Dataset rows.
A flag indicating whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def fit(dataset: Dataset[_]): SentenceDetectorDLModel

Definition Classes
AnnotatorApproach → Estimator
def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[SentenceDetectorDLModel]

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], paramMap: ParamMap): SentenceDetectorDLModel

Definition Classes
Estimator
Annotations
@Since( "2.0.0" )
def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SentenceDetectorDLModel

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getEpochsNumber: Int

Maximum number of epochs to train
def getExplodeSentences: Boolean

Whether to split sentences into different Dataset rows.
Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.
def getGraphFilename: String
def getImpossiblePenultimates: Array[String]

Get impossible penultimates
def getInputCols: Array[String]

returns
input annotations columns currently used

Definition Classes
HasInputAnnotationCols
def getLazyAnnotator: Boolean

Definition Classes
CanBeLazy
def getModel: String

Get model architecture
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
final def getOutputCol: String

Gets annotation column name going to generate
Gets annotation column name going to generate

Definition Classes
HasOutputAnnotationCol
def getOutputLogsPath: String

Get output logs path
def getParam(paramName: String): Param[Any]

Definition Classes
Params
def getValidationSplit: Float

Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
final def hasDefault[T](param: Param[T]): Boolean

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
val impossiblePenultimates: StringArrayParam

Impossible penultimates
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[AnnotatorType]

Input annotator type : SENTENCE_EMBEDDINGS
Input annotator type : SENTENCE_EMBEDDINGS

Definition Classes
SentenceDetectorDLApproach → HasInputAnnotationCols
final val inputCols: StringArrayParam

columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified
columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

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 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
var modelArchitecture: Param[String]

Model architecture
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
final def notifyAll(): Unit

Definition Classes
AnyRef
def onTrained(model: SentenceDetectorDLModel, spark: SparkSession): Unit

Definition Classes
AnnotatorApproach
val outputAnnotatorType: String

Output annotator type : CATEGORY
Output annotator type : CATEGORY

Definition Classes
SentenceDetectorDLApproach → HasOutputAnnotatorType
final val outputCol: Param[String]

Attributes
protected
Definition Classes
HasOutputAnnotationCol
val outputLogsPath: Param[String]

Path to folder to output logs.
Path to folder to output logs. If no path is specified, no logs are generated
lazy val params: Array[Param[_]]

Definition Classes
Params
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): SentenceDetectorDLApproach.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): SentenceDetectorDLApproach.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): SentenceDetectorDLApproach.this.type

Definition Classes
Params
final def setDefault(paramPairs: ParamPair[_]*): SentenceDetectorDLApproach.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): SentenceDetectorDLApproach.this.type

Attributes
protected
Definition Classes
Params
def setEpochsNumber(epochs: Int): SentenceDetectorDLApproach.this.type

Maximum number of epochs to train
def setExplodeSentences(value: Boolean): SentenceDetectorDLApproach.this.type

Whether to split sentences into different Dataset rows.
Whether to split sentences into different Dataset rows. Useful for higher parallelism in fat rows. Defaults to false.
def setGraphFile(graphFilename: String): SentenceDetectorDLApproach.this.type
def setImpossiblePenultimates(impossiblePenultimates: Array[String]): SentenceDetectorDLApproach.this.type

Set impossible penultimates
final def setInputCols(value: String*): SentenceDetectorDLApproach.this.type

Definition Classes
HasInputAnnotationCols
final def setInputCols(value: Array[String]): SentenceDetectorDLApproach.this.type

Overrides required annotators column if different than default
Overrides required annotators column if different than default

Definition Classes
HasInputAnnotationCols
def setLazyAnnotator(value: Boolean): SentenceDetectorDLApproach.this.type

Definition Classes
CanBeLazy
def setModel(modelArchitecture: String): SentenceDetectorDLApproach.this.type

Set architecture
final def setOutputCol(value: String): SentenceDetectorDLApproach.this.type

Overrides annotation column name when transforming
Overrides annotation column name when transforming

Definition Classes
HasOutputAnnotationCol
def setOutputLogsPath(outputLogsPath: String): SentenceDetectorDLApproach.this.type

Set the output log path
def setValidationSplit(validationSplit: Float): SentenceDetectorDLApproach.this.type

Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def train(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): SentenceDetectorDLModel

Definition Classes
SentenceDetectorDLApproach → AnnotatorApproach
final def transformSchema(schema: StructType): StructType

requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()

Definition Classes
AnnotatorApproach → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String

Definition Classes
SentenceDetectorDLApproach → Identifiable
def validate(schema: StructType): Boolean

takes a Dataset and checks to see if all the required annotation types are present.
takes a Dataset and checks to see if all the required annotation types are present.
schema
to be validated
returns
True if all the required types are present, else false

Attributes
protected
Definition Classes
AnnotatorApproach
val validationSplit: FloatParam

Choose the proportion of training dataset to be validated against the model on each Epoch.
Choose the proportion of training dataset to be validated against the model on each Epoch. The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
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( ... )
def write: MLWriter

Definition Classes
DefaultParamsWritable → MLWritable

Related Doc: package sentence_detector_dl

class SentenceDetectorDLApproach extends AnnotatorApproach[SentenceDetectorDLModel]

Instance Constructors

new SentenceDetectorDLApproach()

new SentenceDetectorDLApproach(uid: String)

Type Members

type AnnotatorType = String

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

def _fit(dataset: Dataset[_], recursiveStages: Option[PipelineModel]): SentenceDetectorDLModel

final def asInstanceOf[T0]: T0

def beforeTraining(spark: SparkSession): Unit

final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

final def clear(param: Param[_]): SentenceDetectorDLApproach.this.type

def clone(): AnyRef

final def copy(extra: ParamMap): Estimator[SentenceDetectorDLModel]

def copyValues[T <: Params](to: T, extra: ParamMap): T

final def defaultCopy[T <: Params](extra: ParamMap): T

val description: String

val epochsNumber: IntParam

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

def explodeSentences: BooleanParam

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def finalize(): Unit

final def fit(dataset: Dataset[_]): SentenceDetectorDLModel

def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[SentenceDetectorDLModel]

def fit(dataset: Dataset[_], paramMap: ParamMap): SentenceDetectorDLModel

def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SentenceDetectorDLModel

final def get[T](param: Param[T]): Option[T]

final def getClass(): Class[_]

final def getDefault[T](param: Param[T]): Option[T]

def getEpochsNumber: Int

def getExplodeSentences: Boolean

def getGraphFilename: String

def getImpossiblePenultimates: Array[String]

def getInputCols: Array[String]

def getLazyAnnotator: Boolean

def getModel: String

final def getOrDefault[T](param: Param[T]): T

final def getOutputCol: String

def getOutputLogsPath: String

def getParam(paramName: String): Param[Any]

def getValidationSplit: Float

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hashCode(): Int

val impossiblePenultimates: StringArrayParam

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

val inputAnnotatorTypes: Array[AnnotatorType]

final val inputCols: StringArrayParam

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

val lazyAnnotator: BooleanParam

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

var modelArchitecture: Param[String]

def msgHelper(schema: StructType): String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit