Packages

class SpanBertCorefModel extends AnnotatorModel[SpanBertCorefModel] with HasSimpleAnnotate[SpanBertCorefModel] with WriteTensorflowModel with HasEmbeddingsProperties with HasStorageRef with HasCaseSensitiveProperties with HasEngine

A coreference resolution model based on SpanBert

A coreference resolution model identifies expressions which refer to the same entity in a text. For example, given a sentence "John told Mary he would like to borrow a book from her." the model will link "he" to "John" and "her" to "Mary".

This model is based on SpanBert, which is fine-tuned on the OntoNotes 5.0 data set.

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

val dependencyParserApproach = SpanBertCorefModel.pretrained()
  .setInputCols("sentence", "token")
  .setOutputCol("corefs")

The default model is "spanbert_base_coref", if no name is provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see the Examples

References:

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 sentence = new SentenceDetector()
  .setInputCols("document")
  .setOutputCol("sentence")

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

val corefResolution = SpanBertCorefModel.pretrained()
  .setInputCols("sentence", "token")
  .setOutputCol("corefs")

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

val data = Seq(
  "John told Mary he would like to borrow a book from her."
).toDF("text")

val result = pipeline.fit(data).transform(data)

result.selectExpr(""explode(corefs) AS coref"")
  .selectExpr("coref.result as token", "coref.metadata").show(truncate = false)
+-----+------------------------------------------------------------------------------------+
|token|metadata                                                                            |
+-----+------------------------------------------------------------------------------------+
|John |{head.sentence -> -1, head -> ROOT, head.begin -> -1, head.end -> -1, sentence -> 0}|
|he   |{head.sentence -> 0, head -> John, head.begin -> 0, head.end -> 3, sentence -> 0}   |
|Mary |{head.sentence -> -1, head -> ROOT, head.begin -> -1, head.end -> -1, sentence -> 0}|
|her  |{head.sentence -> 0, head -> Mary, head.begin -> 10, head.end -> 13, sentence -> 0} |
+-----+------------------------------------------------------------------------------------+
Linear Supertypes
HasEngine, HasCaseSensitiveProperties, HasStorageRef, HasEmbeddingsProperties, HasProtectedParams, WriteTensorflowModel, HasSimpleAnnotate[SpanBertCorefModel], AnnotatorModel[SpanBertCorefModel], CanBeLazy, RawAnnotator[SpanBertCorefModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[SpanBertCorefModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. SpanBertCorefModel
  2. HasEngine
  3. HasCaseSensitiveProperties
  4. HasStorageRef
  5. HasEmbeddingsProperties
  6. HasProtectedParams
  7. WriteTensorflowModel
  8. HasSimpleAnnotate
  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 SpanBertCorefModel()
  2. new SpanBertCorefModel(uid: String)

Type Members

  1. implicit class ProtectedParam[T] extends Param[T]
    Definition Classes
    HasProtectedParams
  2. 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
  3. 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. val _textGenres: Array[String]
  10. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  12. def annotate(annotations: Seq[Annotation]): 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

    annotations

    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
    SpanBertCorefModel → HasSimpleAnnotate
  13. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  14. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  15. 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
  16. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  17. final def clear(param: Param[_]): SpanBertCorefModel.this.type
    Definition Classes
    Params
  18. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  19. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

  20. def copy(extra: ParamMap): SpanBertCorefModel

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  21. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  22. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  23. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. def dfAnnotate: UserDefinedFunction

    Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column

    Wraps annotate to happen inside SparkSQL user defined functions in order to act with org.apache.spark.sql.Column

    returns

    udf function to be applied to inputCols using this annotator's annotate function as part of ML transformation

    Definition Classes
    HasSimpleAnnotate
  25. val dimension: ProtectedParam[Int]

    Number of embedding dimensions (Default depends on model)

    Number of embedding dimensions (Default depends on model)

    Definition Classes
    HasEmbeddingsProperties
  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 getCaseSensitive: Boolean

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

  45. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. def getDimension: Int

    Definition Classes
    HasEmbeddingsProperties
  47. def getEngine: String

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  49. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  50. def getMaxSegmentLength: Int

  51. def getMaxSentenceLength: Int

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

  57. def getStorageRef: String
    Definition Classes
    HasStorageRef
  58. def getTextGenre: 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]

    Annotator reference id.

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

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

    Max segment length to process (Read-only, depends on model)

  85. val maxSentenceLength: IntParam

    Max sentence length to process (Default: 128)

  86. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  87. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  88. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  90. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    SpanBertCorefModel → ParamsAndFeaturesWritable
  91. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  92. val outputAnnotatorType: AnnotatorType
    Definition Classes
    SpanBertCorefModel → HasOutputAnnotatorType
  93. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  94. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  95. var parent: Estimator[SpanBertCorefModel]
    Definition Classes
    Model
  96. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  97. def sentenceEndTokenId: Int

  98. def sentenceStartTokenId: Int
  99. def set[T](param: ProtectedParam[T], value: T): SpanBertCorefModel.this.type

    Sets the value for a protected Param.

    Sets the value for a protected Param.

    If the parameter was already set, it will not be set again. Default values do not count as a set value and can be overridden.

    T

    Type of the parameter

    param

    Protected parameter to set

    value

    Value for the parameter

    returns

    This object

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

    Definition Classes
    HasCaseSensitiveProperties
  108. def setConfigProtoBytes(bytes: Array[Int]): SpanBertCorefModel.this.type

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

    Definition Classes
    HasEmbeddingsProperties
  116. final def setInputCols(value: String*): SpanBertCorefModel.this.type
    Definition Classes
    HasInputAnnotationCols
  117. def setInputCols(value: Array[String]): SpanBertCorefModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  118. def setLazyAnnotator(value: Boolean): SpanBertCorefModel.this.type
    Definition Classes
    CanBeLazy
  119. def setMaxSegmentLength(value: Int): SpanBertCorefModel.this.type

  120. def setMaxSentenceLength(value: Int): SpanBertCorefModel.this.type

  121. def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: TensorflowWrapper): SpanBertCorefModel
  122. final def setOutputCol(value: String): SpanBertCorefModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

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

  125. def setStorageRef(value: String): SpanBertCorefModel.this.type
    Definition Classes
    HasStorageRef
  126. def setTextGenre(value: String): SpanBertCorefModel.this.type

  127. def setVocabulary(value: Map[String, Int]): SpanBertCorefModel.this.type

  128. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  129. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  130. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  131. val textGenre: Param[String]

    Text genre, one of the following values: bc: Broadcast conversation, default bn: Broadcast news nw: News wire pt: Pivot text: Old Testament and New Testament text tc: Telephone conversation wb: Web data

  132. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  133. def tokenizeSentence(tokens: Seq[TokenizedSentence]): Seq[WordpieceTokenizedSentence]
  134. 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
  135. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  136. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  137. 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
  138. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  139. val uid: String
    Definition Classes
    SpanBertCorefModel → Identifiable
  140. 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
  141. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  142. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with WordPieceEncoder

  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 wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  148. def wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  149. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  150. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  151. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  152. 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 HasCaseSensitiveProperties

Inherited from HasStorageRef

Inherited from HasEmbeddingsProperties

Inherited from HasProtectedParams

Inherited from WriteTensorflowModel

Inherited from HasSimpleAnnotate[SpanBertCorefModel]

Inherited from AnnotatorModel[SpanBertCorefModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[SpanBertCorefModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[SpanBertCorefModel]

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.

Members

Parameter setters

Parameter getters