Packages

class DeBertaForZeroShotClassification extends AnnotatorModel[DeBertaForZeroShotClassification] with HasBatchedAnnotate[DeBertaForZeroShotClassification] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasCaseSensitiveProperties with HasClassifierActivationProperties with HasEngine with HasCandidateLabelsProperties

DeBertaForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DeBertaForZeroShotClassification models, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is much more flexible.

Note that the model will loop through all provided labels. So the more labels you have, the longer this process will take.

Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model.

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

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

The default model is "deberta_base_zero_shot_classifier_mnli_anli_v3", 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.

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 = DeBertaForZeroShotClassification .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

DeBertaForZeroShotClassification for sequence-level classification

Annotators Main Page for a list of transformer based classifiers

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

  1. new DeBertaForZeroShotClassification()

    Annotator reference id.

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

  2. new DeBertaForZeroShotClassification(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
    DeBertaForZeroShotClassification → 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 candidateLabels: StringArrayParam

    Candidate labels for classification, you can set candidateLabels dynamically during the runtime

    Candidate labels for classification, you can set candidateLabels dynamically during the runtime

    Definition Classes
    HasCandidateLabelsProperties
  18. 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
  19. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  20. final def clear(param: Param[_]): DeBertaForZeroShotClassification.this.type
    Definition Classes
    Params
  21. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  22. 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 DeBerta (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.

  23. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

  24. val contradictionIdParam: IntParam

    Definition Classes
    HasCandidateLabelsProperties
  25. def copy(extra: ParamMap): DeBertaForZeroShotClassification

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  26. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  27. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  28. 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
  29. val entailmentIdParam: IntParam

    Definition Classes
    HasCandidateLabelsProperties
  30. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  32. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  33. def explainParams(): String
    Definition Classes
    Params
  34. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  35. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Definition Classes
    HasClassifierActivationProperties
  46. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  47. def getCandidateLabels: Array[String]

    Definition Classes
    HasCandidateLabelsProperties
  48. def getCaseSensitive: Boolean

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

    Returns labels used to train this model

  51. def getCoalesceSentences: Boolean

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

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

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  56. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  57. def getMaxSentenceLength: Int

  58. def getModelIfNotSet: DeBertaClassification

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

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

    Input Annotator Types: DOCUMENT, TOKEN

    Input Annotator Types: DOCUMENT, TOKEN

    Definition Classes
    DeBertaForZeroShotClassification → HasInputAnnotationCols
  70. 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
  71. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  72. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  73. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  74. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  75. val labels: MapFeature[String, Int]

    Labels used to decode predicted IDs back to string tags

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

    Max sentence length to process (Default: 128)

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

    Output Annotator Types: CATEGORY

    Output Annotator Types: CATEGORY

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

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

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  111. def setCandidateLabels(value: Array[String]): DeBertaForZeroShotClassification.this.type

    Definition Classes
    HasCandidateLabelsProperties
  112. def setCaseSensitive(value: Boolean): DeBertaForZeroShotClassification.this.type

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

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

    Definition Classes
    DeBertaForZeroShotClassification → HasCaseSensitiveProperties
  113. def setCoalesceSentences(value: Boolean): DeBertaForZeroShotClassification.this.type

  114. def setConfigProtoBytes(bytes: Array[Int]): DeBertaForZeroShotClassification.this.type

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

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

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

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

  126. def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: Option[TensorflowWrapper], onnxWrapper: Option[OnnxWrapper], spp: SentencePieceWrapper): DeBertaForZeroShotClassification

  127. def setMultilabel(value: Boolean): DeBertaForZeroShotClassification.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): DeBertaForZeroShotClassification.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

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

  131. def setThreshold(threshold: Float): DeBertaForZeroShotClassification.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. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  133. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  134. 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
  135. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  136. 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
  137. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  138. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  139. 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
  140. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  141. val uid: String
    Definition Classes
    DeBertaForZeroShotClassification → Identifiable
  142. 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
  143. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  144. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  145. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  146. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  147. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  148. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  149. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOnnxModel
  150. def writeSentencePieceModel(path: String, spark: SparkSession, spp: SentencePieceWrapper, suffix: String, filename: String): Unit
    Definition Classes
    WriteSentencePieceModel
  151. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  152. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  153. 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 HasCandidateLabelsProperties

Inherited from HasEngine

Inherited from HasClassifierActivationProperties

Inherited from HasCaseSensitiveProperties

Inherited from WriteSentencePieceModel

Inherited from WriteOnnxModel

Inherited from WriteTensorflowModel

Inherited from HasBatchedAnnotate[DeBertaForZeroShotClassification]

Inherited from AnnotatorModel[DeBertaForZeroShotClassification]

Inherited from CanBeLazy

Inherited from RawAnnotator[DeBertaForZeroShotClassification]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[DeBertaForZeroShotClassification]

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