Packages

class RoBertaForSequenceClassification extends AnnotatorModel[RoBertaForSequenceClassification] with HasBatchedAnnotate[RoBertaForSequenceClassification] with WriteTensorflowModel with WriteOnnxModel with WriteOpenvinoModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine

RoBertaForSequenceClassification can load RoBERTa 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.

Pretrained models can be loaded with pretrained of the companion object:

val sequenceClassifier = RoBertaForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "roberta_base_sequence_classifier_imdb", 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 RoBertaForSequenceClassification.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val tokenizer = new Tokenizer()
  .setInputCols("document")
  .setOutputCol("token")

val sequenceClassifier = RoBertaForSequenceClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  sequenceClassifier
))

val data = Seq("I loved this movie when I was a child.", "It was pretty boring.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+------+
|result|
+------+
|[pos] |
|[neg] |
+------+
See also

RoBertaForSequenceClassification for sequence-level classification

Annotators Main Page for a list of transformer based classifiers

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

Instance Constructors

  1. new RoBertaForSequenceClassification()

    Annotator reference id.

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

  2. new RoBertaForSequenceClassification(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
    RoBertaForSequenceClassification → 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[_]): RoBertaForSequenceClassification.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. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  23. def copy(extra: ParamMap): RoBertaForSequenceClassification

    requirement for annotators copies

    requirement for annotators copies

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Definition Classes
    HasClassifierActivationProperties
  43. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  44. def getCaseSensitive: Boolean

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

    Returns labels used to train this model

  47. def getCoalesceSentences: Boolean

  48. def getConfigProtoBytes: Option[Array[Byte]]

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

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  52. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  53. def getMaxSentenceLength: Int

  54. def getModelIfNotSet: RoBertaClassification

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

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

    Input Annotator Types: DOCUMENT, TOKEN

    Input Annotator Types: DOCUMENT, TOKEN

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

    Labels used to decode predicted IDs back to string tags

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

    Max sentence length to process (Default: 128)

  86. val merges: MapFeature[(String, String), Int]

    Holding merges.txt coming from RoBERTa model

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

    Output Annotator Types: CATEGORY

    Output Annotator Types: CATEGORY

    Definition Classes
    RoBertaForSequenceClassification → HasOutputAnnotatorType
  95. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  96. def padTokenId: Int
  97. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  98. var parent: Estimator[RoBertaForSequenceClassification]
    Definition Classes
    Model
  99. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  100. def sentenceEndTokenId: Int
  101. def sentenceStartTokenId: Int
  102. def set[T](feature: StructFeature[T], value: T): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. def set[T](feature: SetFeature[T], value: Set[T]): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  105. def set[T](feature: ArrayFeature[T], value: Array[T]): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  106. final def set(paramPair: ParamPair[_]): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  107. final def set(param: String, value: Any): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  108. final def set[T](param: Param[T], value: T): RoBertaForSequenceClassification.this.type
    Definition Classes
    Params
  109. def setActivation(value: String): RoBertaForSequenceClassification.this.type

    Definition Classes
    HasClassifierActivationProperties
  110. def setBatchSize(size: Int): RoBertaForSequenceClassification.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  111. def setCaseSensitive(value: Boolean): RoBertaForSequenceClassification.this.type

    Whether to lowercase tokens or not (Default: false).

    Whether to lowercase tokens or not (Default: false).

    Definition Classes
    RoBertaForSequenceClassification → HasCaseSensitiveProperties
  112. def setCoalesceSentences(value: Boolean): RoBertaForSequenceClassification.this.type

  113. def setConfigProtoBytes(bytes: Array[Int]): RoBertaForSequenceClassification.this.type

  114. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  115. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  116. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  117. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  118. final def setDefault(paramPairs: ParamPair[_]*): RoBertaForSequenceClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  119. final def setDefault[T](param: Param[T], value: T): RoBertaForSequenceClassification.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  120. final def setInputCols(value: String*): RoBertaForSequenceClassification.this.type
    Definition Classes
    HasInputAnnotationCols
  121. def setInputCols(value: Array[String]): RoBertaForSequenceClassification.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  122. def setLabels(value: Map[String, Int]): RoBertaForSequenceClassification.this.type

  123. def setLazyAnnotator(value: Boolean): RoBertaForSequenceClassification.this.type
    Definition Classes
    CanBeLazy
  124. def setMaxSentenceLength(value: Int): RoBertaForSequenceClassification.this.type

  125. def setMerges(value: Map[(String, String), Int]): RoBertaForSequenceClassification.this.type

  126. def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: Option[TensorflowWrapper], onnxWrapper: Option[OnnxWrapper], openvinoWrapper: Option[OpenvinoWrapper]): RoBertaForSequenceClassification

  127. def setMultilabel(value: Boolean): RoBertaForSequenceClassification.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
  128. final def setOutputCol(value: String): RoBertaForSequenceClassification.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  129. def setParent(parent: Estimator[RoBertaForSequenceClassification]): RoBertaForSequenceClassification
    Definition Classes
    Model
  130. def setSignatures(value: Map[String, String]): RoBertaForSequenceClassification.this.type

  131. def setThreshold(threshold: Float): RoBertaForSequenceClassification.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
  132. def setVocabulary(value: Map[String, Int]): RoBertaForSequenceClassification.this.type

  133. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  134. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  135. 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
  136. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  137. 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
  138. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  139. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  140. 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
  141. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  142. val uid: String
    Definition Classes
    RoBertaForSequenceClassification → Identifiable
  143. 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
  144. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with WordPieceEncoder

  145. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  146. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  147. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  148. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  149. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  150. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  151. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOnnxModel
  152. def writeOpenvinoModel(path: String, spark: SparkSession, openvinoWrapper: OpenvinoWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOpenvinoModel
  153. def writeOpenvinoModels(path: String, spark: SparkSession, ovWrappersWithNames: Seq[(OpenvinoWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOpenvinoModel
  154. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  155. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  156. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from HasEngine

Inherited from HasClassifierActivationProperties

Inherited from HasCaseSensitiveProperties

Inherited from WriteOpenvinoModel

Inherited from WriteOnnxModel

Inherited from WriteTensorflowModel

Inherited from HasBatchedAnnotate[RoBertaForSequenceClassification]

Inherited from AnnotatorModel[RoBertaForSequenceClassification]

Inherited from CanBeLazy

Inherited from RawAnnotator[RoBertaForSequenceClassification]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[RoBertaForSequenceClassification]

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