Packages

class BertSentenceEmbeddings extends AnnotatorModel[BertSentenceEmbeddings] with HasBatchedAnnotate[BertSentenceEmbeddings] with WriteTensorflowModel with HasEmbeddingsProperties with HasStorageRef with HasCaseSensitiveProperties with HasEngine

Sentence-level embeddings using BERT. BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture.

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

val embeddings = BertSentenceEmbeddings.pretrained()
  .setInputCols("sentence")
  .setOutputCol("sentence_bert_embeddings")

The default model is "sent_small_bert_L2_768", if no name is provided.

For available pretrained models please see the Models Hub.

For extended examples of usage, see the Spark NLP Workshop and the BertSentenceEmbeddingsTestSpec.

Sources :

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/google-research/bert

Paper abstract

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.SentenceDetector
import com.johnsnowlabs.nlp.embeddings.BertSentenceEmbeddings
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 = BertSentenceEmbeddings.pretrained("sent_small_bert_L2_128")
  .setInputCols("sentence")
  .setOutputCol("sentence_bert_embeddings")

val embeddingsFinisher = new EmbeddingsFinisher()
  .setInputCols("sentence_bert_embeddings")
  .setOutputCols("finished_embeddings")
  .setOutputAsVector(true)

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

val data = Seq("John loves apples. Mary loves oranges. John loves Mary.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[-0.8951074481010437,0.13753940165042877,0.3108254075050354,-1.65693199634552...|
|[-0.6180210709571838,-0.12179657071828842,-0.191165953874588,-1.4497021436691...|
|[-0.822715163230896,0.7568016648292542,-0.1165061742067337,-1.59048593044281,...|
+--------------------------------------------------------------------------------+
See also

BertEmbeddings for token-level embeddings

BertForSequenceClassification for embeddings with a sequence classification layer on top

Annotators Main Page for a list of transformer based embeddings

Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. BertSentenceEmbeddings
  2. HasEngine
  3. HasCaseSensitiveProperties
  4. HasStorageRef
  5. HasEmbeddingsProperties
  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
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new BertSentenceEmbeddings()
  2. new BertSentenceEmbeddings(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
    BertSentenceEmbeddingsAnnotatorModel
  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
    BertSentenceEmbeddingsHasBatchedAnnotate
  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[_]): BertSentenceEmbeddings.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()

  21. def copy(extra: ParamMap): BertSentenceEmbeddings

    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. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  24. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. val dimension: IntParam

    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 getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  43. def getCaseSensitive: Boolean

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

    ConfigProto from tensorflow, serialized into byte array.

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

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

    Definition Classes
    HasEmbeddingsProperties
  48. def getEngine: String

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  50. def getIsLong: Boolean

    get isLong

  51. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  52. def getMaxSentenceLength: Int

    Max sentence length to process (Default: 128)

  53. def getModelIfNotSet: TensorflowBert

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

  58. def getStorageRef: String
    Definition Classes
    HasStorageRef
  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
    BertSentenceEmbeddingsHasInputAnnotationCols
  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. val isLong: BooleanParam

    Use Long type instead of Int type for inputs (Default: false)

  70. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  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. 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
  91. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  92. val outputAnnotatorType: AnnotatorType
  93. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  94. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  95. var parent: Estimator[BertSentenceEmbeddings]
    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](feature: StructFeature[T], value: T): BertSentenceEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  100. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): BertSentenceEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  101. def set[T](feature: SetFeature[T], value: Set[T]): BertSentenceEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  102. def set[T](feature: ArrayFeature[T], value: Array[T]): BertSentenceEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. final def set(paramPair: ParamPair[_]): BertSentenceEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  104. final def set(param: String, value: Any): BertSentenceEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. final def set[T](param: Param[T], value: T): BertSentenceEmbeddings.this.type
    Definition Classes
    Params
  106. def setBatchSize(size: Int): BertSentenceEmbeddings.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  107. def setCaseSensitive(value: Boolean): BertSentenceEmbeddings.this.type

    Whether to lowercase tokens or not

    Whether to lowercase tokens or not

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

    ConfigProto from tensorflow, serialized into byte array.

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

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

    Set Embeddings dimensions for the BERT model Only possible to set this when the first time is saved dimension is not changeable, it comes from BERT config file

    Set Embeddings dimensions for the BERT model Only possible to set this when the first time is saved dimension is not changeable, it comes from BERT config file

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

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  118. def setIsLong(value: Boolean): BertSentenceEmbeddings.this.type

    set isLong

  119. def setLazyAnnotator(value: Boolean): BertSentenceEmbeddings.this.type
    Definition Classes
    CanBeLazy
  120. def setMaxSentenceLength(value: Int): BertSentenceEmbeddings.this.type

    Max sentence length to process (Default: 128)

  121. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper): BertSentenceEmbeddings.this.type

  122. final def setOutputCol(value: String): BertSentenceEmbeddings.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

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

  125. def setStorageRef(value: String): BertSentenceEmbeddings.this.type
    Definition Classes
    HasStorageRef
  126. def setVocabulary(value: Map[String, Int]): BertSentenceEmbeddings.this.type

    Vocabulary used to encode the words to ids with WordPieceEncoder

  127. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  128. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  129. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  130. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  131. def tokenize(sentences: Seq[Sentence]): Seq[WordpieceTokenizedSentence]
  132. 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
  133. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  134. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  135. 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
  136. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  137. val uid: String
    Definition Classes
    BertSentenceEmbeddings → Identifiable
  138. 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
  139. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  140. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with WordPieceEncoder

  141. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  142. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  143. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  144. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  145. def wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  146. def wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  147. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  148. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  149. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  150. 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 HasStorageRef

Inherited from HasEmbeddingsProperties

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[BertSentenceEmbeddings]

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