Class/Object

com.johnsnowlabs.nlp.annotators.classifier.dl

XlnetForTokenClassification

Related Docs: object XlnetForTokenClassification | package dl

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class XlnetForTokenClassification extends AnnotatorModel[XlnetForTokenClassification] with HasBatchedAnnotate[XlnetForTokenClassification] with WriteTensorflowModel with WriteSentencePieceModel with HasCaseSensitiveProperties

XlnetForTokenClassification can load XLNet Models with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.

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

val tokenClassifier = XlnetForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")

The default model is "xlnet_base_token_classifier_conll03", if no name is provided.

For available pretrained models please see the Models Hub.

and the XlnetForTokenClassificationTestSpec. Models from the HuggingFace πŸ€— Transformers library are also compatible with Spark NLP πŸš€. The Spark NLP Workshop example shows how to import them 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 tokenClassifier = XlnetForTokenClassification.pretrained()
  .setInputCols("token", "document")
  .setOutputCol("label")
  .setCaseSensitive(true)

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

val data = Seq("John Lenon was born in London and lived in Paris. My name is Sarah and I live in London").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("label.result").show(false)
+------------------------------------------------------------------------------------+
|result                                                                              |
+------------------------------------------------------------------------------------+
|[B-PER, I-PER, O, O, O, B-LOC, O, O, O, B-LOC, O, O, O, O, B-PER, O, O, O, O, B-LOC]|
+------------------------------------------------------------------------------------+
See also

Annotators Main Page for a list of transformer based classifiers

XlnetForTokenClassification for token-level classification

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

Instance Constructors

  1. new XlnetForTokenClassification()

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    Annotator reference id.

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

  2. new XlnetForTokenClassification(uid: String)

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    uid

    required uid for storing annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

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    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

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    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef β†’ Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef β†’ Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
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    Params
  4. def $$[T](feature: StructFeature[T]): T

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    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef β†’ Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  12. def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]

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    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
    XlnetForTokenClassification β†’ HasBatchedAnnotate
  13. def batchProcess(rows: Iterator[_]): Iterator[Row]

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    Definition Classes
    HasBatchedAnnotate
  14. val batchSize: IntParam

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    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[_]

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  16. val caseSensitive: BooleanParam

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    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

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  18. final def clear(param: Param[_]): XlnetForTokenClassification.this.type

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    Definition Classes
    Params
  19. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  20. val configProtoBytes: IntArrayParam

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    ConfigProto from tensorflow, serialized into byte array.

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

  21. def copy(extra: ParamMap): XlnetForTokenClassification

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    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator β†’ Model β†’ Transformer β†’ PipelineStage β†’ Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  23. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  24. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  25. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef β†’ Any
  26. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  27. def explainParams(): String

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    Definition Classes
    Params
  28. def extraValidate(structType: StructType): Boolean

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  29. def extraValidateMsg: String

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    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  30. final def extractParamMap(): ParamMap

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    Definition Classes
    Params
  31. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params
  32. val features: ArrayBuffer[Feature[_, _, _]]

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    Definition Classes
    HasFeatures
  33. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  34. def get[T](feature: StructFeature[T]): Option[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  36. def get[T](feature: SetFeature[T]): Option[Set[T]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  37. def get[T](feature: ArrayFeature[T]): Option[Array[T]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  38. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  39. def getBatchSize: Int

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    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  40. def getCaseSensitive: Boolean

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    Definition Classes
    HasCaseSensitiveProperties
  41. final def getClass(): Class[_]

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    Definition Classes
    AnyRef β†’ Any
  42. def getClasses: Array[String]

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    Returns labels used to train this model

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

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  44. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  45. def getInputCols: Array[String]

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    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  46. def getLazyAnnotator: Boolean

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    Definition Classes
    CanBeLazy
  47. def getMaxSentenceLength: Int

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  48. def getModelIfNotSet: TensorflowXlnetClassification

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  49. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  50. final def getOutputCol: String

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    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  51. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  52. def getSignatures: Option[Map[String, String]]

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  53. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params
  54. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params
  55. def hasParent: Boolean

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    Definition Classes
    Model
  56. def hashCode(): Int

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    Definition Classes
    AnyRef β†’ Any
  57. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  58. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  59. val inputAnnotatorTypes: Array[String]

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    Input Annotator Types: DOCUMENT, TOKEN

    Input Annotator Types: DOCUMENT, TOKEN

    Definition Classes
    XlnetForTokenClassification β†’ HasInputAnnotationCols
  60. final val inputCols: StringArrayParam

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    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
  61. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params
  62. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  63. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  64. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  65. val labels: MapFeature[String, Int]

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    Labels used to decode predicted IDs back to string tags

  66. val lazyAnnotator: BooleanParam

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    Definition Classes
    CanBeLazy
  67. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  68. def logDebug(msg: β‡’ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  69. def logDebug(msg: β‡’ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  70. def logError(msg: β‡’ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  71. def logError(msg: β‡’ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  72. def logInfo(msg: β‡’ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  73. def logInfo(msg: β‡’ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  74. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  75. def logTrace(msg: β‡’ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  76. def logTrace(msg: β‡’ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  77. def logWarning(msg: β‡’ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  78. def logWarning(msg: β‡’ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  79. val maxSentenceLength: IntParam

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    Max sentence length to process (Default: 128)

  80. def msgHelper(schema: StructType): String

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  81. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  82. final def notify(): Unit

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    Definition Classes
    AnyRef
  83. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  84. def onWrite(path: String, spark: SparkSession): Unit

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  85. val optionalInputAnnotatorTypes: Array[String]

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    Definition Classes
    HasInputAnnotationCols
  86. val outputAnnotatorType: AnnotatorType

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    Output Annotator Types: WORD_EMBEDDINGS

    Output Annotator Types: WORD_EMBEDDINGS

    Definition Classes
    XlnetForTokenClassification β†’ HasOutputAnnotatorType
  87. final val outputCol: Param[String]

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    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  88. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  89. var parent: Estimator[XlnetForTokenClassification]

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    Definition Classes
    Model
  90. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  91. def set[T](feature: StructFeature[T], value: T): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  92. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  93. def set[T](feature: SetFeature[T], value: Set[T]): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def set[T](feature: ArrayFeature[T], value: Array[T]): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  95. final def set(paramPair: ParamPair[_]): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    Params
  96. final def set(param: String, value: Any): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    Params
  97. final def set[T](param: Param[T], value: T): XlnetForTokenClassification.this.type

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    Definition Classes
    Params
  98. def setBatchSize(size: Int): XlnetForTokenClassification.this.type

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    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  99. def setCaseSensitive(value: Boolean): XlnetForTokenClassification.this.type

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    Whether to lowercase tokens or not

    Whether to lowercase tokens or not

    Definition Classes
    XlnetForTokenClassification β†’ HasCaseSensitiveProperties
  100. def setConfigProtoBytes(bytes: Array[Int]): XlnetForTokenClassification.this.type

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  101. def setDefault[T](feature: StructFeature[T], value: () β‡’ T): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def setDefault[K, V](feature: MapFeature[K, V], value: () β‡’ Map[K, V]): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def setDefault[T](feature: SetFeature[T], value: () β‡’ Set[T]): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  104. def setDefault[T](feature: ArrayFeature[T], value: () β‡’ Array[T]): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  105. final def setDefault(paramPairs: ParamPair[_]*): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    Params
  106. final def setDefault[T](param: Param[T], value: T): XlnetForTokenClassification.this.type

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    Attributes
    protected
    Definition Classes
    Params
  107. final def setInputCols(value: String*): XlnetForTokenClassification.this.type

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    Definition Classes
    HasInputAnnotationCols
  108. def setInputCols(value: Array[String]): XlnetForTokenClassification.this.type

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    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

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

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  110. def setLazyAnnotator(value: Boolean): XlnetForTokenClassification.this.type

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    Definition Classes
    CanBeLazy
  111. def setMaxSentenceLength(value: Int): XlnetForTokenClassification.this.type

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  112. def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: TensorflowWrapper, spp: SentencePieceWrapper): XlnetForTokenClassification

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  113. final def setOutputCol(value: String): XlnetForTokenClassification.this.type

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    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  114. def setParent(parent: Estimator[XlnetForTokenClassification]): XlnetForTokenClassification

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    Definition Classes
    Model
  115. def setSignatures(value: Map[String, String]): XlnetForTokenClassification.this.type

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  116. val signatures: MapFeature[String, String]

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    It contains TF model signatures for the laded saved model

  117. final def synchronized[T0](arg0: β‡’ T0): T0

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    Definition Classes
    AnyRef
  118. def toString(): String

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    Definition Classes
    Identifiable β†’ AnyRef β†’ Any
  119. final def transform(dataset: Dataset[_]): DataFrame

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    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 β†’ Transformer
  120. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  121. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  122. final def transformSchema(schema: StructType): StructType

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    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator β†’ PipelineStage
  123. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  124. val uid: String

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    required uid for storing annotator to disk

    required uid for storing annotator to disk

    Definition Classes
    XlnetForTokenClassification β†’ Identifiable
  125. def validate(schema: StructType): Boolean

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    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
  126. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  127. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  128. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  129. def wrapColumnMetadata(col: Column): Column

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  130. def write: MLWriter

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    Definition Classes
    ParamsAndFeaturesWritable β†’ DefaultParamsWritable β†’ MLWritable
  131. def writeSentencePieceModel(path: String, spark: SparkSession, spp: SentencePieceWrapper, suffix: String, filename: String): Unit

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    Definition Classes
    WriteSentencePieceModel
  132. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit

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    Definition Classes
    WriteTensorflowModel
  133. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit

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    Definition Classes
    WriteTensorflowModel
  134. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit

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    Definition Classes
    WriteTensorflowModel

Inherited from WriteSentencePieceModel

Inherited from WriteTensorflowModel

Inherited from CanBeLazy

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[XlnetForTokenClassification]

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