com.johnsnowlabs.nlp.annotators.classifier.dl
MPNetForSequenceClassification
Companion object MPNetForSequenceClassification
class MPNetForSequenceClassification extends AnnotatorModel[MPNetForSequenceClassification] with HasBatchedAnnotate[MPNetForSequenceClassification] with WriteOnnxModel with WriteOpenvinoModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine
MPNetForSequenceClassification can load MPNet Models with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.
Note that currently, only SetFit models can be imported.
Pretrained models can be loaded with pretrained of the companion object:
val sequenceClassifier = MPNetForSequenceClassification.pretrained() .setInputCols("token", "document") .setOutputCol("label")
The default model is "mpnet_sequence_classifier_ukr_message", if no name is provided.
For available pretrained models please see the Models Hub.
To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see MPNetForSequenceClassificationTestSpec.
Example
import com.johnsnowlabs.nlp.base._ import com.johnsnowlabs.nlp.annotator._ import org.apache.spark.ml.Pipeline import spark.implicits._ val document = new DocumentAssembler() .setInputCol("text") .setOutputCol("document") val tokenizer = new Tokenizer() .setInputCols(Array("document")) .setOutputCol("token") val sequenceClassifier = MPNetForSequenceClassification .pretrained() .setInputCols(Array("document", "token")) .setOutputCol("label") val texts = Seq( "I love driving my car.", "The next bus will arrive in 20 minutes.", "pineapple on pizza is the worst 🤮") val data = texts.toDF("text") val pipeline = new Pipeline().setStages(Array(document, tokenizer, sequenceClassifier)) val pipelineModel = pipeline.fit(data) val results = pipelineModel.transform(data) results.select("label.result").show() +--------------------+ | result| +--------------------+ | [TRANSPORT/CAR]| |[TRANSPORT/MOVEMENT]| | [FOOD]| +--------------------+
- See also
MPNetForSequenceClassification for sequence-level classification
Annotators Main Page for a list of transformer based classifiers
- Grouped
- Alphabetic
- By Inheritance
- MPNetForSequenceClassification
- HasEngine
- HasClassifierActivationProperties
- HasCaseSensitiveProperties
- WriteOpenvinoModel
- WriteOnnxModel
- HasBatchedAnnotate
- AnnotatorModel
- CanBeLazy
- RawAnnotator
- HasOutputAnnotationCol
- HasInputAnnotationCols
- HasOutputAnnotatorType
- ParamsAndFeaturesWritable
- HasFeatures
- DefaultParamsWritable
- MLWritable
- Model
- Transformer
- PipelineStage
- Logging
- Params
- Serializable
- Identifiable
- AnyRef
- Any
- Hide All
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- Public
- Protected
Instance Constructors
Type Members
- type AnnotationContent = Seq[Row]
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI
- Attributes
- protected
- Definition Classes
- AnnotatorModel
- 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 _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
- val activation: Param[String]
Whether to enable caching DataFrames or RDDs during the training (Default depends on model).
Whether to enable caching DataFrames or RDDs during the training (Default depends on model).
- Definition Classes
- HasClassifierActivationProperties
- def afterAnnotate(dataset: DataFrame): DataFrame
- Attributes
- protected
- Definition Classes
- AnnotatorModel
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- 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
- MPNetForSequenceClassification → HasBatchedAnnotate
- def batchProcess(rows: Iterator[_]): Iterator[Row]
- Definition Classes
- HasBatchedAnnotate
- val batchSize: IntParam
Size of every batch (Default depends on model).
Size of every batch (Default depends on model).
- Definition Classes
- HasBatchedAnnotate
- def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
- Attributes
- protected
- Definition Classes
- AnnotatorModel
- val caseSensitive: BooleanParam
Whether to ignore case in index lookups (Default depends on model)
Whether to ignore case in index lookups (Default depends on model)
- Definition Classes
- HasCaseSensitiveProperties
- final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
- final def clear(param: Param[_]): MPNetForSequenceClassification.this.type
- Definition Classes
- Params
- def clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.CloneNotSupportedException]) @HotSpotIntrinsicCandidate() @native()
- val coalesceSentences: BooleanParam
Instead of 1 class per sentence (if inputCols is sentence) output 1 class per document by averaging probabilities in all sentences (Default:
false).Instead of 1 class per sentence (if inputCols is sentence) output 1 class per document by averaging probabilities in all sentences (Default:
false).Due to max sequence length limit in almost all transformer models such as BERT (512 tokens), this parameter helps feeding all the sentences into the model and averaging all the probabilities for the entire document instead of probabilities per sentence.
- def copy(extra: ParamMap): MPNetForSequenceClassification
requirement for annotators copies
requirement for annotators copies
- Definition Classes
- RawAnnotator → Model → Transformer → 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 engine: Param[String]
This param is set internally once via loadSavedModel.
This param is set internally once via loadSavedModel. That's why there is no setter
- Definition Classes
- HasEngine
- final def eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- def equals(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef → Any
- def explainParam(param: Param[_]): String
- Definition Classes
- Params
- def explainParams(): String
- Definition Classes
- Params
- def extraValidate(structType: StructType): Boolean
- Attributes
- protected
- Definition Classes
- RawAnnotator
- def extraValidateMsg: String
Override for additional custom schema checks
Override for additional custom schema checks
- Attributes
- protected
- Definition Classes
- RawAnnotator
- final def extractParamMap(): ParamMap
- Definition Classes
- Params
- final def extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
- Params
- val features: ArrayBuffer[Feature[_, _, _]]
- Definition Classes
- HasFeatures
- 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 getActivation: String
- Definition Classes
- HasClassifierActivationProperties
- def getBatchSize: Int
Size of every batch.
Size of every batch.
- Definition Classes
- HasBatchedAnnotate
- def getCaseSensitive: Boolean
- Definition Classes
- HasCaseSensitiveProperties
- final def getClass(): Class[_ <: AnyRef]
- Definition Classes
- AnyRef → Any
- Annotations
- @HotSpotIntrinsicCandidate() @native()
- def getClasses: Array[String]
Returns labels used to train this model
- def getCoalesceSentences: Boolean
- final def getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
- def getEngine: String
- Definition Classes
- HasEngine
- def getInputCols: Array[String]
- returns
input annotations columns currently used
- Definition Classes
- HasInputAnnotationCols
- def getLazyAnnotator: Boolean
- Definition Classes
- CanBeLazy
- def getMaxSentenceLength: Int
- def getModelIfNotSet: MPNetClassification
- 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 getParam(paramName: String): Param[Any]
- Definition Classes
- Params
- def getSignatures: Option[Map[String, String]]
- final def hasDefault[T](param: Param[T]): Boolean
- Definition Classes
- Params
- def hasParam(paramName: String): Boolean
- Definition Classes
- Params
- def hasParent: Boolean
- Definition Classes
- Model
- def hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @HotSpotIntrinsicCandidate() @native()
- def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
- Attributes
- protected
- Definition Classes
- Logging
- def initializeLogIfNecessary(isInterpreter: Boolean): Unit
- Attributes
- protected
- Definition Classes
- Logging
- val inputAnnotatorTypes: Array[String]
Input Annotator Types: DOCUMENT, TOKEN
Input Annotator Types: DOCUMENT, TOKEN
- Definition Classes
- MPNetForSequenceClassification → 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 labels: MapFeature[String, Int]
Labels used to decode predicted IDs back to string tags
- val lazyAnnotator: BooleanParam
- Definition Classes
- CanBeLazy
- def log: Logger
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logDebug(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logError(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logInfo(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logName: String
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logTrace(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(msg: => String, throwable: Throwable): Unit
- Attributes
- protected
- Definition Classes
- Logging
- def logWarning(msg: => String): Unit
- Attributes
- protected
- Definition Classes
- Logging
- val maxSentenceLength: IntParam
Max sentence length to process (Default:
128) - def msgHelper(schema: StructType): String
- Attributes
- protected
- Definition Classes
- HasInputAnnotationCols
- val multilabel: BooleanParam
Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0).
Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class
- Definition Classes
- HasClassifierActivationProperties
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @HotSpotIntrinsicCandidate() @native()
- final def notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @HotSpotIntrinsicCandidate() @native()
- def onWrite(path: String, spark: SparkSession): Unit
- Definition Classes
- MPNetForSequenceClassification → ParamsAndFeaturesWritable
- val optionalInputAnnotatorTypes: Array[String]
- Definition Classes
- HasInputAnnotationCols
- val outputAnnotatorType: AnnotatorType
Output Annotator Types: CATEGORY
Output Annotator Types: CATEGORY
- Definition Classes
- MPNetForSequenceClassification → HasOutputAnnotatorType
- final val outputCol: Param[String]
- Attributes
- protected
- Definition Classes
- HasOutputAnnotationCol
- lazy val params: Array[Param[_]]
- Definition Classes
- Params
- var parent: Estimator[MPNetForSequenceClassification]
- Definition Classes
- Model
- def save(path: String): Unit
- Definition Classes
- MLWritable
- Annotations
- @throws("If the input path already exists but overwrite is not enabled.") @Since("1.6.0")
- def sentenceEndTokenId: Int
- def sentenceStartTokenId: Int
- def set[T](feature: StructFeature[T], value: T): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
- def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
- def set[T](feature: SetFeature[T], value: Set[T]): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
- def set[T](feature: ArrayFeature[T], value: Array[T]): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
- final def set(paramPair: ParamPair[_]): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def set(param: String, value: Any): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def set[T](param: Param[T], value: T): MPNetForSequenceClassification.this.type
- Definition Classes
- Params
- def setActivation(value: String): MPNetForSequenceClassification.this.type
- Definition Classes
- HasClassifierActivationProperties
- def setBatchSize(size: Int): MPNetForSequenceClassification.this.type
Size of every batch.
Size of every batch.
- Definition Classes
- HasBatchedAnnotate
- def setCaseSensitive(value: Boolean): MPNetForSequenceClassification.this.type
Whether to lowercase tokens or not
Whether to lowercase tokens or not
- Definition Classes
- MPNetForSequenceClassification → HasCaseSensitiveProperties
- def setCoalesceSentences(value: Boolean): MPNetForSequenceClassification.this.type
- def setDefault[T](feature: StructFeature[T], value: () => T): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
- def setDefault[K, V](feature: MapFeature[K, V], value: () => Map[K, V]): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
- def setDefault[T](feature: SetFeature[T], value: () => Set[T]): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
- def setDefault[T](feature: ArrayFeature[T], value: () => Array[T]): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
- final def setDefault(paramPairs: ParamPair[_]*): MPNetForSequenceClassification.this.type
- Attributes
- protected
- Definition Classes
- Params
- final def setDefault[T](param: Param[T], value: T): MPNetForSequenceClassification.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
- final def setInputCols(value: String*): MPNetForSequenceClassification.this.type
- Definition Classes
- HasInputAnnotationCols
- def setInputCols(value: Array[String]): MPNetForSequenceClassification.this.type
Overrides required annotators column if different than default
Overrides required annotators column if different than default
- Definition Classes
- HasInputAnnotationCols
- def setLabels(value: Map[String, Int]): MPNetForSequenceClassification.this.type
- def setLazyAnnotator(value: Boolean): MPNetForSequenceClassification.this.type
- Definition Classes
- CanBeLazy
- def setMaxSentenceLength(value: Int): MPNetForSequenceClassification.this.type
- def setModelIfNotSet(spark: SparkSession, onnxWrapper: Option[OnnxWrapper], openvinoWrapper: Option[OpenvinoWrapper]): MPNetForSequenceClassification
- def setMultilabel(value: Boolean): MPNetForSequenceClassification.this.type
Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0).
Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class
- Definition Classes
- HasClassifierActivationProperties
- final def setOutputCol(value: String): MPNetForSequenceClassification.this.type
Overrides annotation column name when transforming
Overrides annotation column name when transforming
- Definition Classes
- HasOutputAnnotationCol
- def setParent(parent: Estimator[MPNetForSequenceClassification]): MPNetForSequenceClassification
- Definition Classes
- Model
- def setSignatures(value: Map[String, String]): MPNetForSequenceClassification.this.type
- def setThreshold(threshold: Float): MPNetForSequenceClassification.this.type
Choose the threshold to determine which logits are considered to be positive or negative.
Choose the threshold to determine which logits are considered to be positive or negative. (Default:
0.5f). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process.- Definition Classes
- HasClassifierActivationProperties
- def setVocabulary(value: Map[String, Int]): MPNetForSequenceClassification.this.type
- 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
- val threshold: FloatParam
Choose the threshold to determine which logits are considered to be positive or negative.
Choose the threshold to determine which logits are considered to be positive or negative. (Default:
0.5f). The value should be between 0.0 and 1.0. Changing the threshold value will affect the resulting labels and can be used to adjust the balance between precision and recall in the classification process.- Definition Classes
- HasClassifierActivationProperties
- def toString(): String
- Definition Classes
- Identifiable → AnyRef → Any
- final def transform(dataset: Dataset[_]): DataFrame
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content
- dataset
Dataset[Row]
- 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
- @varargs() @Since("2.0.0")
- final def transformSchema(schema: StructType): StructType
requirement for pipeline transformation validation.
requirement for pipeline transformation validation. It is called on fit()
- Definition Classes
- RawAnnotator → PipelineStage
- def transformSchema(schema: StructType, logging: Boolean): StructType
- Attributes
- protected
- Definition Classes
- PipelineStage
- Annotations
- @DeveloperApi()
- val uid: String
- Definition Classes
- MPNetForSequenceClassification → 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
- RawAnnotator
- val vocabulary: MapFeature[String, Int]
Vocabulary used to encode the words to ids with WordPieceEncoder
- final def wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- final def wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException]) @native()
- final def wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.InterruptedException])
- 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 writeOpenvinoModel(path: String, spark: SparkSession, openvinoWrapper: OpenvinoWrapper, suffix: String, fileName: String): Unit
- Definition Classes
- WriteOpenvinoModel
- def writeOpenvinoModels(path: String, spark: SparkSession, ovWrappersWithNames: Seq[(OpenvinoWrapper, String)], suffix: String): Unit
- Definition Classes
- WriteOpenvinoModel
Deprecated Value Members
- def finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws(classOf[java.lang.Throwable]) @Deprecated
- Deprecated
(Since version 9)
Inherited from HasEngine
Inherited from HasClassifierActivationProperties
Inherited from HasCaseSensitiveProperties
Inherited from WriteOpenvinoModel
Inherited from WriteOnnxModel
Inherited from HasBatchedAnnotate[MPNetForSequenceClassification]
Inherited from AnnotatorModel[MPNetForSequenceClassification]
Inherited from CanBeLazy
Inherited from RawAnnotator[MPNetForSequenceClassification]
Inherited from HasOutputAnnotationCol
Inherited from HasInputAnnotationCols
Inherited from HasOutputAnnotatorType
Inherited from ParamsAndFeaturesWritable
Inherited from HasFeatures
Inherited from DefaultParamsWritable
Inherited from MLWritable
Inherited from Model[MPNetForSequenceClassification]
Inherited from Transformer
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.
Annotator types
Required input and expected output annotator types