Packages

class UniversalSentenceEncoder extends AnnotatorModel[UniversalSentenceEncoder] with HasBatchedAnnotate[UniversalSentenceEncoder] with HasEmbeddingsProperties with HasStorageRef with WriteTensorflowModel with HasEngine

The Universal Sentence Encoder encodes text into high dimensional vectors that can be used for text classification, semantic similarity, clustering and other natural language tasks.

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

val useEmbeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("sentence")
  .setOutputCol("sentence_embeddings")

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

For extended examples of usage, see the Examples and the UniversalSentenceEncoderTestSpec.

References:

Universal Sentence Encoder

https://tfhub.dev/google/universal-sentence-encoder/2

Paper abstract:

We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.SentenceDetector
import com.johnsnowlabs.nlp.embeddings.UniversalSentenceEncoder
import com.johnsnowlabs.nlp.EmbeddingsFinisher
import org.apache.spark.ml.Pipeline

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

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

val embeddings = UniversalSentenceEncoder.pretrained()
  .setInputCols("sentence")
  .setOutputCol("sentence_embeddings")

val embeddingsFinisher = new EmbeddingsFinisher()
  .setInputCols("sentence_embeddings")
  .setOutputCols("finished_embeddings")
  .setOutputAsVector(true)
  .setCleanAnnotations(false)

val pipeline = new Pipeline()
  .setStages(Array(
    documentAssembler,
    sentence,
    embeddings,
    embeddingsFinisher
  ))

val data = Seq("This is a sentence.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[0.04616805538535118,0.022307956591248512,-0.044395286589860916,-0.0016493503...|
+--------------------------------------------------------------------------------+
See also

Annotators Main Page for a list of transformer based embeddings

Linear Supertypes
HasEngine, WriteTensorflowModel, HasStorageRef, HasEmbeddingsProperties, HasProtectedParams, HasBatchedAnnotate[UniversalSentenceEncoder], AnnotatorModel[UniversalSentenceEncoder], CanBeLazy, RawAnnotator[UniversalSentenceEncoder], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[UniversalSentenceEncoder], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. UniversalSentenceEncoder
  2. HasEngine
  3. WriteTensorflowModel
  4. HasStorageRef
  5. HasEmbeddingsProperties
  6. HasProtectedParams
  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 UniversalSentenceEncoder()

    Annotator reference id.

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

  2. new UniversalSentenceEncoder(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. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    UniversalSentenceEncoder → 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
    UniversalSentenceEncoder → 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. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  17. final def clear(param: Param[_]): UniversalSentenceEncoder.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): UniversalSentenceEncoder

    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. val dimension: ProtectedParam[Int]

    Number of embedding dimensions (Default: 512)

    Number of embedding dimensions (Default: 512)

    Definition Classes
    UniversalSentenceEncoder → HasEmbeddingsProperties
  25. 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
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  29. def explainParams(): String
    Definition Classes
    Params
  30. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  31. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Size of every batch.

    Size of every batch.

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

    ConfigProto from tensorflow, serialized into byte array.

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

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

    Definition Classes
    HasEmbeddingsProperties
  46. def getEngine: String

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  48. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  49. def getLoadSP: Boolean

    Whether to load SentencePiece ops file which is required only by multi-lingual models.

  50. def getModelIfNotSet: USE

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  53. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  54. def getStorageRef: String
    Definition Classes
    HasStorageRef
  55. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  56. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  57. def hasParent: Boolean
    Definition Classes
    Model
  58. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  59. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  60. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : DOCUMENT

    Input annotator type : DOCUMENT

    Definition Classes
    UniversalSentenceEncoder → HasInputAnnotationCols
  62. 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
  63. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  64. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  65. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  66. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  67. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  68. val loadSP: ProtectedParam[Boolean]

    Whether to load SentencePiece ops file which is required only by multi-lingual models (Default: false).

    Whether to load SentencePiece ops file which is required only by multi-lingual models (Default: false). This is not changeable after it's set with a pretrained model nor it is compatible with Windows.

  69. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  70. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  77. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  82. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  83. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  84. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  85. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    UniversalSentenceEncoder → ParamsAndFeaturesWritable
  86. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  87. val outputAnnotatorType: AnnotatorType

    Output annotator type : SENTENCE_EMBEDDINGS

    Output annotator type : SENTENCE_EMBEDDINGS

    Definition Classes
    UniversalSentenceEncoder → HasOutputAnnotatorType
  88. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  89. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  90. var parent: Estimator[UniversalSentenceEncoder]
    Definition Classes
    Model
  91. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  92. def set[T](param: ProtectedParam[T], value: T): UniversalSentenceEncoder.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
  93. def set[T](feature: StructFeature[T], value: T): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def set[T](feature: SetFeature[T], value: Set[T]): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. def set[T](feature: ArrayFeature[T], value: Array[T]): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. final def set(paramPair: ParamPair[_]): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def set(param: String, value: Any): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    Params
  99. final def set[T](param: Param[T], value: T): UniversalSentenceEncoder.this.type
    Definition Classes
    Params
  100. def setBatchSize(size: Int): UniversalSentenceEncoder.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  101. def setConfigProtoBytes(bytes: Array[Int]): UniversalSentenceEncoder.this.type

    ConfigProto from tensorflow, serialized into byte array.

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

  102. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  105. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  106. final def setDefault(paramPairs: ParamPair[_]*): UniversalSentenceEncoder.this.type
    Attributes
    protected
    Definition Classes
    Params
  107. final def setDefault[T](param: Param[T], value: T): UniversalSentenceEncoder.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  108. def setDimension(value: Int): UniversalSentenceEncoder.this.type

    Definition Classes
    HasEmbeddingsProperties
  109. final def setInputCols(value: String*): UniversalSentenceEncoder.this.type
    Definition Classes
    HasInputAnnotationCols
  110. def setInputCols(value: Array[String]): UniversalSentenceEncoder.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  111. def setLazyAnnotator(value: Boolean): UniversalSentenceEncoder.this.type
    Definition Classes
    CanBeLazy
  112. def setLoadSP(value: Boolean): UniversalSentenceEncoder.this.type

    Whether to load SentencePiece ops file which is required only by multi-lingual models.

  113. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper): UniversalSentenceEncoder.this.type

  114. final def setOutputCol(value: String): UniversalSentenceEncoder.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  115. def setParent(parent: Estimator[UniversalSentenceEncoder]): UniversalSentenceEncoder
    Definition Classes
    Model
  116. def setStorageRef(value: String): UniversalSentenceEncoder.this.type
    Definition Classes
    HasStorageRef
  117. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  118. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  119. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  120. 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
  121. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  122. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  123. 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
  124. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  125. val uid: String
    Definition Classes
    UniversalSentenceEncoder → Identifiable
  126. 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
  127. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  128. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  129. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  130. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  131. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  132. def wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  133. def wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  134. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  135. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  136. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  137. 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 WriteTensorflowModel

Inherited from HasStorageRef

Inherited from HasEmbeddingsProperties

Inherited from HasProtectedParams

Inherited from HasBatchedAnnotate[UniversalSentenceEncoder]

Inherited from AnnotatorModel[UniversalSentenceEncoder]

Inherited from CanBeLazy

Inherited from RawAnnotator[UniversalSentenceEncoder]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[UniversalSentenceEncoder]

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