Packages

class ZeroShotNerModel extends RoBertaForQuestionAnswering

ZeroShotNerModel implements zero shot named entity recognition by utilizing RoBERTa transformer models fine tuned on a question answering task.

Its input is a list of document annotations and it automatically generates questions which are used to recognize entities. The definitions of entities is given by a dictionary structures, specifying a set of questions for each entity. The model is based on RoBertaForQuestionAnswering.

For more extended examples see the Examples

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

val zeroShotNer = ZeroShotNerModel.pretrained()
  .setInputCols("document")
  .setOutputCol("zer_shot_ner")

For available pretrained models please see the Models Hub.

Example

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

 val sentenceDetector = new SentenceDetector()
   .setInputCols(Array("document"))
   .setOutputCol("sentences")

 val zeroShotNer = ZeroShotNerModel
   .pretrained()
   .setEntityDefinitions(
     Map(
       "NAME" -> Array("What is his name?", "What is her name?"),
       "CITY" -> Array("Which city?")))
   .setPredictionThreshold(0.01f)
   .setInputCols("sentences")
   .setOutputCol("zero_shot_ner")

 val pipeline = new Pipeline()
   .setStages(Array(
     documentAssembler,
     sentenceDetector,
     zeroShotNer))

 val model = pipeline.fit(Seq("").toDS.toDF("text"))
 val results = model.transform(
   Seq("Clara often travels between New York and Paris.").toDS.toDF("text"))

 results
   .selectExpr("document", "explode(zero_shot_ner) AS entity")
   .select(
     col("entity.result"),
     col("entity.metadata.word"),
     col("entity.metadata.sentence"),
     col("entity.begin"),
     col("entity.end"),
     col("entity.metadata.confidence"),
     col("entity.metadata.question"))
   .show(truncate=false)

+------+-----+--------+-----+---+----------+------------------+
|result|word |sentence|begin|end|confidence|question          |
+------+-----+--------+-----+---+----------+------------------+
|B-CITY|Paris|0       |41   |45 |0.78655756|Which is the city?|
|B-CITY|New  |0       |28   |30 |0.29346612|Which city?       |
|I-CITY|York |0       |32   |35 |0.29346612|Which city?       |
+------+-----+--------+-----+---+----------+------------------+
See also

https://arxiv.org/abs/1907.11692 for details about the RoBERTa transformer

RoBertaForQuestionAnswering for the SparkNLP implementation of RoBERTa question answering

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

Instance Constructors

  1. new ZeroShotNerModel()

    Annotator reference id.

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

  2. new ZeroShotNerModel(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. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  12. 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
    ZeroShotNerModel → RoBertaForQuestionAnswering → HasBatchedAnnotate
  13. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotate
  14. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

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

    ConfigProto from tensorflow, serialized into byte array.

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

    Definition Classes
    RoBertaForQuestionAnswering
  21. def copy(extra: ParamMap): RoBertaForQuestionAnswering

    requirement for annotators copies

    requirement for annotators copies

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  41. def getCaseSensitive: Boolean

    Definition Classes
    HasCaseSensitiveProperties
  42. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  43. def getConfigProtoBytes: Option[Array[Byte]]

    Definition Classes
    RoBertaForQuestionAnswering
  44. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  45. def getEngine: String

    Definition Classes
    HasEngine
  46. def getEntities: Array[String]

    Get the list of entities which are recognized

  47. def getEntityDefinitionsStr: Array[String]
  48. def getIgnoreEntities: Array[String]

    Get the list of questions to catch the distractor entity

  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

    Definition Classes
    RoBertaForQuestionAnswering
  52. def getModelIfNotSet: RoBertaClassification

    Definition Classes
    ZeroShotNerModel → RoBertaForQuestionAnswering
  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 getPredictionThreshold: Float

    Get the minimum entity prediction score

  57. def getSignatures: Option[Map[String, String]]

    Definition Classes
    RoBertaForQuestionAnswering
  58. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  59. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  60. def hasParent: Boolean
    Definition Classes
    Model
  61. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  62. var ignoreEntities: StringArrayParam
  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

    Input Annotator Types: DOCUMENT

    Definition Classes
    ZeroShotNerModel → RoBertaForQuestionAnswering → 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 isTokenInEntity(token: Annotation, entity: Annotation): Boolean
  71. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  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. def maskEntity(document: Annotation, entity: Annotation): String
  86. val maskSymbol: String
  87. val maxSentenceLength: IntParam

    Max sentence length to process (Default: 128)

    Max sentence length to process (Default: 128)

    Definition Classes
    RoBertaForQuestionAnswering
  88. val merges: MapFeature[(String, String), Int]

    Holding merges.txt coming from RoBERTa model

    Holding merges.txt coming from RoBERTa model

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

    Output Annotator Types: NAMED_ENTITY

    Output Annotator Types: NAMED_ENTITY

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

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  113. def setCaseSensitive(value: Boolean): ZeroShotNerModel.this.type

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

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

    Definition Classes
    RoBertaForQuestionAnswering → HasCaseSensitiveProperties
  114. def setConfigProtoBytes(bytes: Array[Int]): ZeroShotNerModel.this.type

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

    Set definitions of named entities

  122. def setEntityDefinitions(definitions: Map[String, Array[String]]): ZeroShotNerModel.this.type

    Set definitions of named entities

  123. def setIgnoreEntities(value: Array[String]): ZeroShotNerModel.this.type

    Set the list of questions to catch the distractor entity

  124. final def setInputCols(value: String*): ZeroShotNerModel.this.type
    Definition Classes
    HasInputAnnotationCols
  125. def setInputCols(value: Array[String]): ZeroShotNerModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  126. def setLazyAnnotator(value: Boolean): ZeroShotNerModel.this.type
    Definition Classes
    CanBeLazy
  127. def setMaxSentenceLength(value: Int): ZeroShotNerModel.this.type

    Definition Classes
    RoBertaForQuestionAnswering
  128. def setMerges(value: Map[(String, String), Int]): ZeroShotNerModel.this.type

    Definition Classes
    RoBertaForQuestionAnswering
  129. def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: Option[TensorflowWrapper], onnxWrapper: Option[OnnxWrapper]): ZeroShotNerModel

    Definition Classes
    ZeroShotNerModel → RoBertaForQuestionAnswering
  130. final def setOutputCol(value: String): ZeroShotNerModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  131. def setParent(parent: Estimator[RoBertaForQuestionAnswering]): RoBertaForQuestionAnswering
    Definition Classes
    Model
  132. def setPredictionThreshold(value: Float): ZeroShotNerModel.this.type

    Set the minimum entity prediction score

  133. def setSignatures(value: Map[String, String]): ZeroShotNerModel.this.type

    Definition Classes
    RoBertaForQuestionAnswering
  134. def setVocabulary(value: Map[String, Int]): ZeroShotNerModel.this.type

    Definition Classes
    RoBertaForQuestionAnswering
  135. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

    It contains TF model signatures for the laded saved model

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

    Vocabulary used to encode the words to ids with WordPieceEncoder

    Vocabulary used to encode the words to ids with WordPieceEncoder

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

Inherited from HasEngine

Inherited from HasCaseSensitiveProperties

Inherited from WriteOnnxModel

Inherited from WriteTensorflowModel

Inherited from HasBatchedAnnotate[RoBertaForQuestionAnswering]

Inherited from AnnotatorModel[RoBertaForQuestionAnswering]

Inherited from CanBeLazy

Inherited from RawAnnotator[RoBertaForQuestionAnswering]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[RoBertaForQuestionAnswering]

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