Packages

class MPNetForSequenceClassification extends AnnotatorModel[MPNetForSequenceClassification] with HasBatchedAnnotate[MPNetForSequenceClassification] with WriteOnnxModel 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

Linear Supertypes
HasEngine, HasClassifierActivationProperties, HasCaseSensitiveProperties, WriteOnnxModel, HasBatchedAnnotate[MPNetForSequenceClassification], AnnotatorModel[MPNetForSequenceClassification], CanBeLazy, RawAnnotator[MPNetForSequenceClassification], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[MPNetForSequenceClassification], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. MPNetForSequenceClassification
  2. HasEngine
  3. HasClassifierActivationProperties
  4. HasCaseSensitiveProperties
  5. WriteOnnxModel
  6. HasBatchedAnnotate
  7. AnnotatorModel
  8. CanBeLazy
  9. RawAnnotator
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. HasOutputAnnotatorType
  13. ParamsAndFeaturesWritable
  14. HasFeatures
  15. DefaultParamsWritable
  16. MLWritable
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new MPNetForSequenceClassification()

    Annotator reference id.

    Annotator reference id. Used to identify elements in metadata or to refer to this annotator type

  2. new MPNetForSequenceClassification(uid: String)

    uid

    required uid for storing annotator to disk

Type Members

  1. 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
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. 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
  11. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. 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
  14. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotate
  15. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

    Definition Classes
    HasBatchedAnnotate
  16. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  17. 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
  18. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  19. final def clear(param: Param[_]): MPNetForSequenceClassification.this.type
    Definition Classes
    Params
  20. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  21. 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.

  22. def copy(extra: ParamMap): MPNetForSequenceClassification

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  23. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. 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
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  29. def explainParams(): String
    Definition Classes
    Params
  30. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  31. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  32. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  33. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  34. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  35. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  36. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  37. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  41. def getActivation: String

    Definition Classes
    HasClassifierActivationProperties
  42. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  43. def getCaseSensitive: Boolean

    Definition Classes
    HasCaseSensitiveProperties
  44. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. def getClasses: Array[String]

    Returns labels used to train this model

  46. def getCoalesceSentences: Boolean

  47. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  48. def getEngine: String

    Definition Classes
    HasEngine
  49. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  50. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  51. def getMaxSentenceLength: Int

  52. def getModelIfNotSet: MPNetClassification

  53. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  54. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  55. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  56. def getSignatures: Option[Map[String, String]]

  57. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  58. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  59. def hasParent: Boolean
    Definition Classes
    Model
  60. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  61. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  62. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. val inputAnnotatorTypes: Array[String]

    Input Annotator Types: DOCUMENT, TOKEN

    Input Annotator Types: DOCUMENT, TOKEN

    Definition Classes
    MPNetForSequenceClassification → HasInputAnnotationCols
  64. 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
  65. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  66. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  67. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  68. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  69. val labels: MapFeature[String, Int]

    Labels used to decode predicted IDs back to string tags

  70. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  71. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  72. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  79. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. val maxSentenceLength: IntParam

    Max sentence length to process (Default: 128)

  84. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  85. 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
  86. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  87. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  88. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    MPNetForSequenceClassification → ParamsAndFeaturesWritable
  90. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  91. val outputAnnotatorType: AnnotatorType

    Output Annotator Types: CATEGORY

    Output Annotator Types: CATEGORY

    Definition Classes
    MPNetForSequenceClassification → HasOutputAnnotatorType
  92. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  93. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  94. var parent: Estimator[MPNetForSequenceClassification]
    Definition Classes
    Model
  95. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  96. def sentenceEndTokenId: Int

  97. def sentenceStartTokenId: Int

  98. def set[T](feature: StructFeature[T], value: T): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  99. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  100. def set[T](feature: SetFeature[T], value: Set[T]): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  101. def set[T](feature: ArrayFeature[T], value: Array[T]): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. final def set(paramPair: ParamPair[_]): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  103. final def set(param: String, value: Any): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  104. final def set[T](param: Param[T], value: T): MPNetForSequenceClassification.this.type
    Definition Classes
    Params
  105. def setActivation(value: String): MPNetForSequenceClassification.this.type

    Definition Classes
    HasClassifierActivationProperties
  106. def setBatchSize(size: Int): MPNetForSequenceClassification.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  107. def setCaseSensitive(value: Boolean): MPNetForSequenceClassification.this.type

    Whether to lowercase tokens or not

    Whether to lowercase tokens or not

    Definition Classes
    MPNetForSequenceClassification → HasCaseSensitiveProperties
  108. def setCoalesceSentences(value: Boolean): MPNetForSequenceClassification.this.type

  109. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  110. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. final def setDefault(paramPairs: ParamPair[_]*): MPNetForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  114. final def setDefault[T](param: Param[T], value: T): MPNetForSequenceClassification.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  115. final def setInputCols(value: String*): MPNetForSequenceClassification.this.type
    Definition Classes
    HasInputAnnotationCols
  116. 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
  117. def setLabels(value: Map[String, Int]): MPNetForSequenceClassification.this.type

  118. def setLazyAnnotator(value: Boolean): MPNetForSequenceClassification.this.type
    Definition Classes
    CanBeLazy
  119. def setMaxSentenceLength(value: Int): MPNetForSequenceClassification.this.type

  120. def setModelIfNotSet(spark: SparkSession, onnxWrapper: Option[OnnxWrapper]): MPNetForSequenceClassification

  121. 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
  122. final def setOutputCol(value: String): MPNetForSequenceClassification.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  123. def setParent(parent: Estimator[MPNetForSequenceClassification]): MPNetForSequenceClassification
    Definition Classes
    Model
  124. def setSignatures(value: Map[String, String]): MPNetForSequenceClassification.this.type

  125. 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
  126. def setVocabulary(value: Map[String, Int]): MPNetForSequenceClassification.this.type

  127. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  128. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  129. 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
  130. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  131. 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
  132. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  133. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  134. 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
  135. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  136. val uid: String
    Definition Classes
    MPNetForSequenceClassification → Identifiable
  137. 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
  138. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with WordPieceEncoder

  139. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  140. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  141. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  142. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  143. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  144. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  145. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String, dataFileSuffix: String = "_data"): Unit
    Definition Classes
    WriteOnnxModel

Inherited from HasEngine

Inherited from HasClassifierActivationProperties

Inherited from HasCaseSensitiveProperties

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 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

Members

Parameter setters

Parameter getters