class ElmoEmbeddings extends AnnotatorModel[ElmoEmbeddings] with HasSimpleAnnotate[ElmoEmbeddings] with WriteTensorflowModel with HasEmbeddingsProperties with HasStorageRef with HasCaseSensitiveProperties

Embeddings from a language model trained on the 1 Billion Word Benchmark.

Note that this is a very computationally expensive module compared to word embedding modules that only perform embedding lookups. The use of an accelerator is recommended.

word_emb: the character-based word representations with shape [batch_size, max_length, 512]. == word_emb

lstm_outputs1: the first LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs1

lstm_outputs2: the second LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs2

elmo: the weighted sum of the 3 layers, where the weights are trainable. This tensor has shape [batch_size, max_length, 1024] == elmo

See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/ElmoEmbeddingsTestSpec.scala for further reference on how to use this API.

Sources :

https://tfhub.dev/google/elmo/3

https://arxiv.org/abs/1802.05365

Paper abstract :

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pre-trained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

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

Instance Constructors

  1. new ElmoEmbeddings()

    Annotator reference id.

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

  2. new ElmoEmbeddings(uid: String)

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
    ElmoEmbeddingsAnnotatorModel
  11. 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
    ElmoEmbeddingsHasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. val batchSize: IntParam

    Batch size.

    Batch size. Large values allows faster processing but requires more memory.

  14. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  15. val caseSensitive: BooleanParam
    Definition Classes
    HasCaseSensitiveProperties
  16. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  17. final def clear(param: Param[_]): ElmoEmbeddings.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): ElmoEmbeddings

    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: IntParam
    Definition Classes
    HasEmbeddingsProperties
  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 getCaseSensitive: Boolean
    Definition Classes
    HasCaseSensitiveProperties
  42. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  43. def getConfigProtoBytes: Option[Array[Byte]]
  44. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  45. def getDimension: Int
    Definition Classes
    HasEmbeddingsProperties
  46. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  47. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  48. def getModelIfNotSet: TensorflowElmo
  49. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  50. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

    Function used to set the embedding output layer of the ELMO model word_emb: the character-based word representations with shape [batch_size, max_length, 512].

    Function used to set the embedding output layer of the ELMO model word_emb: the character-based word representations with shape [batch_size, max_length, 512]. == word_emb lstm_outputs1: the first LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs1 lstm_outputs2: the second LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs2 elmo: the weighted sum of the 3 layers, where the weights are trainable. This tensor has shape [batch_size, max_length, 1024] == elmo

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

    Output annotator type : TOKEN

    Output annotator type : TOKEN

    Definition Classes
    ElmoEmbeddingsHasInputAnnotationCols
  61. 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
  62. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  63. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  64. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  65. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  66. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  67. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  68. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  75. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  80. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  81. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  82. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  83. def onWrite(path: String, spark: SparkSession): Unit
  84. val outputAnnotatorType: AnnotatorType

    Output annotator type : WORD_EMBEDDINGS

    Output annotator type : WORD_EMBEDDINGS

    Definition Classes
    ElmoEmbeddingsHasOutputAnnotatorType
  85. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  86. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  87. var parent: Estimator[ElmoEmbeddings]
    Definition Classes
    Model
  88. val poolingLayer: Param[String]

    Set ELMO pooling layer to: word_emb, lstm_outputs1, lstm_outputs2, or elmo

  89. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  90. def set[T](feature: StructFeature[T], value: T): ElmoEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  91. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): ElmoEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  92. def set[T](feature: SetFeature[T], value: Set[T]): ElmoEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  93. def set[T](feature: ArrayFeature[T], value: Array[T]): ElmoEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. final def set(paramPair: ParamPair[_]): ElmoEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  95. final def set(param: String, value: Any): ElmoEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  96. final def set[T](param: Param[T], value: T): ElmoEmbeddings.this.type
    Definition Classes
    Params
  97. def setBatchSize(size: Int): ElmoEmbeddings.this.type

    Large values allows faster processing but requires more memory.

  98. def setCaseSensitive(value: Boolean): ElmoEmbeddings.this.type
    Definition Classes
    HasCaseSensitiveProperties
  99. def setConfigProtoBytes(bytes: Array[Int]): ElmoEmbeddings.this.type

    ConfigProto from tensorflow, serialized into byte array.

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

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

    Set Dimension of pooling layer.

    Set Dimension of pooling layer. This is meta for the annotation and will not affect the actual embedding calculation.

    Definition Classes
    ElmoEmbeddingsHasEmbeddingsProperties
  107. final def setInputCols(value: String*): ElmoEmbeddings.this.type
    Definition Classes
    HasInputAnnotationCols
  108. final def setInputCols(value: Array[String]): ElmoEmbeddings.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  109. def setLazyAnnotator(value: Boolean): ElmoEmbeddings.this.type
    Definition Classes
    CanBeLazy
  110. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper): ElmoEmbeddings.this.type

  111. final def setOutputCol(value: String): ElmoEmbeddings.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  112. def setParent(parent: Estimator[ElmoEmbeddings]): ElmoEmbeddings
    Definition Classes
    Model
  113. def setPoolingLayer(layer: String): ElmoEmbeddings.this.type

    Function used to set the embedding output layer of the ELMO model word_emb: the character-based word representations with shape [batch_size, max_length, 512].

    Function used to set the embedding output layer of the ELMO model word_emb: the character-based word representations with shape [batch_size, max_length, 512]. == word_emb lstm_outputs1: the first LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs1 lstm_outputs2: the second LSTM hidden state with shape [batch_size, max_length, 1024]. === lstm_outputs2 elmo: the weighted sum of the 3 layers, where the weights are trainable. This tensor has shape [batch_size, max_length, 1024] == elmo

    layer

    Layer specification

  114. def setStorageRef(value: String): ElmoEmbeddings.this.type
    Definition Classes
    HasStorageRef
  115. val storageRef: Param[String]
    Definition Classes
    HasStorageRef
  116. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  117. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  118. 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
  119. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  120. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  121. 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
  122. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  123. val uid: String
    Definition Classes
    ElmoEmbeddings → Identifiable
  124. 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
  125. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  126. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  127. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  128. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  129. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  130. def wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  131. def wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  132. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  133. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  134. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  135. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from HasStorageRef

Inherited from HasEmbeddingsProperties

Inherited from WriteTensorflowModel

Inherited from AnnotatorModel[ElmoEmbeddings]

Inherited from CanBeLazy

Inherited from RawAnnotator[ElmoEmbeddings]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[ElmoEmbeddings]

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

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters