MultiClassifierDLApproach

Trains a MultiClassifierDL for Multi-label Text Classification.

MultiClassifierDL uses a Bidirectional GRU with a convolutional model that we have built inside TensorFlow and supports up to 100 classes.

For instantiated/pretrained models, see MultiClassifierDLModel.

The input to MultiClassifierDL are Sentence Embeddings such as the state-of-the-art UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings.

In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y (assigning a value of 0 or 1 for each element (label) in y).

Notes:

This annotator requires an array of labels in type of String.
UniversalSentenceEncoder, BertSentenceEmbeddings, or SentenceEmbeddings can be used for the inputCol.

For extended examples of usage, see the Spark NLP Workshop and the MultiClassifierDLTestSpec.

Example

In this example, the training data has the form (Note: labels can be arbitrary)

mr,ref
"name[Alimentum], area[city centre], familyFriendly[no], near[Burger King]",Alimentum is an adult establish found in the city centre area near Burger King.
"name[Alimentum], area[city centre], familyFriendly[yes]",Alimentum is a family-friendly place in the city centre.
...

It needs some pre-processing first, so the labels are of type Array[String]. This can be done like so:

import spark.implicits._
import com.johnsnowlabs.nlp.annotators.classifier.dl.MultiClassifierDLApproach
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
import org.apache.spark.ml.Pipeline
import org.apache.spark.sql.functions.{col, udf}

// Process training data to create text with associated array of labels
def splitAndTrim = udf { labels: String =>
  labels.split(", ").map(x=>x.trim)
}

val smallCorpus = spark.read
  .option("header", true)
  .option("inferSchema", true)
  .option("mode", "DROPMALFORMED")
  .csv("src/test/resources/classifier/e2e.csv")
  .withColumn("labels", splitAndTrim(col("mr")))
  .withColumn("text", col("ref"))
  .drop("mr")

smallCorpus.printSchema()
// root
// |-- ref: string (nullable = true)
// |-- labels: array (nullable = true)
// |    |-- element: string (containsNull = true)

// Then create pipeline for training
val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")
  .setCleanupMode("shrink")

val embeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("document")
  .setOutputCol("embeddings")

val docClassifier = new MultiClassifierDLApproach()
  .setInputCols("embeddings")
  .setOutputCol("category")
  .setLabelColumn("labels")
  .setBatchSize(128)
  .setMaxEpochs(10)
  .setLr(1e-3f)
  .setThreshold(0.5f)
  .setValidationSplit(0.1f)

val pipeline = new Pipeline()
  .setStages(
    Array(
      documentAssembler,
      embeddings,
      docClassifier
    )
  )

val pipelineModel = pipeline.fit(smallCorpus)

See also: SentimentDLApproach for sentiment analysis
ClassifierDLApproach for single-class classification
Multi-label classification on Wikipedia

Linear Supertypes

ParamsAndFeaturesWritable, HasFeatures, AnnotatorApproach[MultiClassifierDLModel], CanBeLazy, DefaultParamsWritable, MLWritable, HasOutputAnnotatorType, HasOutputAnnotationCol, HasInputAnnotationCols, Estimator[MultiClassifierDLModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

Instance Constructors

new MultiClassifierDLApproach()
new MultiClassifierDLApproach(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
def $$[T](feature: StructFeature[T]): T

Attributes
protected
Definition Classes
HasFeatures
def $$[K, V](feature: MapFeature[K, V]): Map[K, V]

Attributes
protected
Definition Classes
HasFeatures
def $$[T](feature: SetFeature[T]): Set[T]

Attributes
protected
Definition Classes
HasFeatures
def $$[T](feature: ArrayFeature[T]): Array[T]

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

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

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

Definition Classes
Any
val batchSize: IntParam

Batch size (Default: 64)
def beforeTraining(spark: SparkSession): Unit

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

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

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val configProtoBytes: IntArrayParam

ConfigProto from tensorflow, serialized into byte array.
ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()
final def copy(extra: ParamMap): Estimator[MultiClassifierDLModel]

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
MultiClassifierDLApproach → AnnotatorApproach
val enableOutputLogs: BooleanParam

Whether to output to annotators log folder (Default: false)
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
final def extractParamMap(): ParamMap

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

Definition Classes
Params
val features: ArrayBuffer[Feature[_, _, _]]

Definition Classes
HasFeatures
def finalize(): Unit

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

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

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

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

Definition Classes
Estimator
Annotations
@Since( "2.0.0" ) @varargs()
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

Batch size (Default: 64)
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getConfigProtoBytes: Option[Array[Byte]]

Tensorflow config Protobytes passed to the TF session
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getEnableOutputLogs: Boolean

Whether to output to annotators log folder (Default: false)
def getInputCols: Array[String]

returns
input annotations columns currently used

Definition Classes
HasInputAnnotationCols
def getLabelColumn: String

Column with label per each document
def getLazyAnnotator: Boolean

Definition Classes
CanBeLazy
def getLr: Float

Learning Rate (Default: 1e-3f)
def getMaxEpochs: Int

Maximum number of epochs to train (Default: 10)
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

Folder path to save training logs (Default: "")
def getParam(paramName: String): Param[Any]

Definition Classes
Params
def getRandomSeed: Int

Random seed
def getShufflePerEpoch: Boolean

Max sequence length to feed into TensorFlow
def getThreshold: Float

The minimum threshold for each label to be accepted (Default: 0.5f)
def getValidationSplit: Float

Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).
Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f). 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
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
MultiClassifierDLApproach → 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 labelColumn: Param[String]

Column with label per each document
val lazyAnnotator: BooleanParam

Definition Classes
CanBeLazy
def loadSavedModel(): TensorflowWrapper
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 lr: FloatParam

Learning Rate (Default: 1e-3f)
val maxEpochs: IntParam

Maximum number of epochs to train (Default: 10)
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: MultiClassifierDLModel, spark: SparkSession): Unit

Definition Classes
AnnotatorApproach
def onWrite(path: String, spark: SparkSession): Unit

Attributes
protected
Definition Classes
ParamsAndFeaturesWritable
val optionalInputAnnotatorTypes: Array[String]

Definition Classes
HasInputAnnotationCols
val outputAnnotatorType: String

Output annotator type : CATEGORY
Output annotator type : CATEGORY

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

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

Folder path to save training logs (Default: "")
lazy val params: Array[Param[_]]

Definition Classes
Params
val randomSeed: IntParam

Random seed for shuffling the dataset (Default: 44)
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
def set[T](feature: StructFeature[T], value: T): MultiClassifierDLApproach.this.type

Attributes
protected
Definition Classes
HasFeatures
def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MultiClassifierDLApproach.this.type

Attributes
protected
Definition Classes
HasFeatures
def set[T](feature: SetFeature[T], value: Set[T]): MultiClassifierDLApproach.this.type

Attributes
protected
Definition Classes
HasFeatures
def set[T](feature: ArrayFeature[T], value: Array[T]): MultiClassifierDLApproach.this.type

Attributes
protected
Definition Classes
HasFeatures
final def set(paramPair: ParamPair[_]): MultiClassifierDLApproach.this.type

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

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

Definition Classes
Params
def setBatchSize(batch: Int): MultiClassifierDLApproach.this.type

Batch size (Default: 64)
def setConfigProtoBytes(bytes: Array[Int]): MultiClassifierDLApproach.this.type

Tensorflow config Protobytes passed to the TF session
def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MultiClassifierDLApproach.this.type

Attributes
protected
Definition Classes
HasFeatures
def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MultiClassifierDLApproach.this.type

Attributes
protected
Definition Classes
HasFeatures
def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MultiClassifierDLApproach.this.type

Attributes
protected
Definition Classes
HasFeatures
def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MultiClassifierDLApproach.this.type

Attributes
protected
Definition Classes
HasFeatures
final def setDefault(paramPairs: ParamPair[_]*): MultiClassifierDLApproach.this.type

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

Attributes
protected
Definition Classes
Params
def setEnableOutputLogs(enableOutputLogs: Boolean): MultiClassifierDLApproach.this.type

Whether to output to annotators log folder (Default: false)
final def setInputCols(value: String*): MultiClassifierDLApproach.this.type

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

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

Definition Classes
HasInputAnnotationCols
def setLabelColumn(column: String): MultiClassifierDLApproach.this.type

Column with label per each document
def setLazyAnnotator(value: Boolean): MultiClassifierDLApproach.this.type

Definition Classes
CanBeLazy
def setLr(lr: Float): MultiClassifierDLApproach.this.type

Learning Rate (Default: 1e-3f)
def setMaxEpochs(epochs: Int): MultiClassifierDLApproach.this.type

Maximum number of epochs to train (Default: 10)
final def setOutputCol(value: String): MultiClassifierDLApproach.this.type

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

Definition Classes
HasOutputAnnotationCol
def setOutputLogsPath(path: String): MultiClassifierDLApproach.this.type

Folder path to save training logs (Default: "")
def setRandomSeed(seed: Int): MultiClassifierDLApproach.this.type

Random seed
def setShufflePerEpoch(value: Boolean): MultiClassifierDLApproach.this.type

shufflePerEpoch
def setThreshold(threshold: Float): MultiClassifierDLApproach.this.type

The minimum threshold for each label to be accepted (Default: 0.5f)
def setValidationSplit(validationSplit: Float): MultiClassifierDLApproach.this.type

Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f).
Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f). The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
def setVerbose(verbose: Level): MultiClassifierDLApproach.this.type

Level of verbosity during training (Default: Verbose.Silent.id)
def setVerbose(verbose: Int): MultiClassifierDLApproach.this.type

Level of verbosity during training (Default: Verbose.Silent.id)
val shufflePerEpoch: BooleanParam

Whether to shuffle the training data on each Epoch (Default: false)
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val threshold: FloatParam

The minimum threshold for each label to be accepted (Default: 0.5f)
def toString(): String

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

Definition Classes
MultiClassifierDLApproach → 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
MultiClassifierDLApproach → 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 (Default: 0.0f).
Choose the proportion of training dataset to be validated against the model on each Epoch (Default: 0.0f). The value should be between 0.0 and 1.0 and by default it is 0.0 and off.
val verbose: IntParam

Level of verbosity during training (Default: Verbose.Silent.id)
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
ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from AnnotatorApproach[MultiClassifierDLModel]

Inherited from CanBeLazy

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from HasOutputAnnotatorType

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from Estimator[MultiClassifierDLModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

Related Doc: package dl

class MultiClassifierDLApproach extends AnnotatorApproach[MultiClassifierDLModel] with ParamsAndFeaturesWritable

Example

Instance Constructors

new MultiClassifierDLApproach()

new MultiClassifierDLApproach(uid: String)

Type Members

type AnnotatorType = String

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

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

def $$[T](feature: StructFeature[T]): T

def $$[K, V](feature: MapFeature[K, V]): Map[K, V]

def $$[T](feature: SetFeature[T]): Set[T]

def $$[T](feature: ArrayFeature[T]): Array[T]

final def ==(arg0: Any): Boolean

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

final def asInstanceOf[T0]: T0

val batchSize: IntParam

def beforeTraining(spark: SparkSession): Unit

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

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

def clone(): AnyRef

val configProtoBytes: IntArrayParam

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

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

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

val description: String

val enableOutputLogs: BooleanParam

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

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

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

val features: ArrayBuffer[Feature[_, _, _]]

def finalize(): Unit

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

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

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

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

def get[T](feature: StructFeature[T]): Option[T]

def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]

def get[T](feature: SetFeature[T]): Option[Set[T]]

def get[T](feature: ArrayFeature[T]): Option[Array[T]]

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

def getBatchSize: Int

final def getClass(): Class[_]

def getConfigProtoBytes: Option[Array[Byte]]

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

def getEnableOutputLogs: Boolean

def getInputCols: Array[String]

def getLabelColumn: String

def getLazyAnnotator: Boolean

def getLr: Float

def getMaxEpochs: Int

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

final def getOutputCol: String

def getOutputLogsPath: String

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

def getRandomSeed: Int

def getShufflePerEpoch: Boolean

def getThreshold: Float

def getValidationSplit: Float

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

def hasParam(paramName: String): Boolean

def hashCode(): Int

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 labelColumn: Param[String]

val lazyAnnotator: BooleanParam

def loadSavedModel(): TensorflowWrapper