Packages

class Word2VecModel extends AnnotatorModel[Word2VecModel] with HasSimpleAnnotate[Word2VecModel] with HasStorageRef with HasEmbeddingsProperties with ParamsAndFeaturesWritable

Word2Vec model that creates vector representations of words in a text corpus.

The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms.

We use Word2Vec implemented in Spark ML. It uses skip-gram model in our implementation and a hierarchical softmax method to train the model. The variable names in the implementation match the original C implementation.

This is the instantiated model of the Word2VecApproach. For training your own model, please see the documentation of that class.

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

val embeddings = Word2VecModel.pretrained()
  .setInputCols("token")
  .setOutputCol("embeddings")

The default model is "word2vec_gigaword_300", if no name is provided.

For available pretrained models please see the Models Hub.

Sources :

For the original C implementation, see https://code.google.com/p/word2vec/

For the research paper, see Efficient Estimation of Word Representations in Vector Space and Distributed Representations of Words and Phrases and their Compositionality.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.{Tokenizer, Word2VecModel}
import com.johnsnowlabs.nlp.EmbeddingsFinisher

import org.apache.spark.ml.Pipeline

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

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

val embeddings = Word2VecModel.pretrained()
  .setInputCols("token")
  .setOutputCol("embeddings")

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

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  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(1, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[0.06222493574023247,0.011579325422644615,0.009919632226228714,0.109361454844...|
+--------------------------------------------------------------------------------+
Linear Supertypes
HasEmbeddingsProperties, HasProtectedParams, HasStorageRef, HasSimpleAnnotate[Word2VecModel], AnnotatorModel[Word2VecModel], CanBeLazy, RawAnnotator[Word2VecModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[Word2VecModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. Word2VecModel
  2. HasEmbeddingsProperties
  3. HasProtectedParams
  4. HasStorageRef
  5. HasSimpleAnnotate
  6. AnnotatorModel
  7. CanBeLazy
  8. RawAnnotator
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. HasOutputAnnotatorType
  12. ParamsAndFeaturesWritable
  13. HasFeatures
  14. DefaultParamsWritable
  15. MLWritable
  16. Model
  17. Transformer
  18. PipelineStage
  19. Logging
  20. Params
  21. Serializable
  22. Serializable
  23. Identifiable
  24. AnyRef
  25. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new Word2VecModel()
  2. new Word2VecModel(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
    Word2VecModel → AnnotatorModel
  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
    Word2VecModel → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Definition Classes
    Word2VecModel → AnnotatorModel
  14. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  15. final def clear(param: Param[_]): Word2VecModel.this.type
    Definition Classes
    Params
  16. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  17. def copy(extra: ParamMap): Word2VecModel

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  18. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  19. def createDatabaseConnection(database: Name): RocksDBConnection
    Definition Classes
    HasStorageRef
  20. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. 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
  22. val dimension: ProtectedParam[Int]

    Number of embedding dimensions (Default depends on model)

    Number of embedding dimensions (Default depends on model)

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Definition Classes
    HasEmbeddingsProperties
  41. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  42. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  43. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  44. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  45. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  46. def getStorageRef: String
    Definition Classes
    HasStorageRef
  47. def getVectorSize: Int

  48. def getVectors: DataFrame
  49. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  50. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  51. def hasParent: Boolean
    Definition Classes
    Model
  52. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  53. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  54. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : TOKEN

    Input annotator type : TOKEN

    Definition Classes
    Word2VecModel → HasInputAnnotationCols
  56. 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
  57. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  58. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  59. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  60. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  61. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  62. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  63. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  70. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  75. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  76. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  77. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  78. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  79. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  80. val outputAnnotatorType: String

    Output annotator type : WORD_EMBEDDINGS

    Output annotator type : WORD_EMBEDDINGS

    Definition Classes
    Word2VecModel → HasOutputAnnotatorType
  81. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  82. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  83. var parent: Estimator[Word2VecModel]
    Definition Classes
    Model
  84. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  85. def set[T](param: ProtectedParam[T], value: T): Word2VecModel.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
  86. def set[T](feature: StructFeature[T], value: T): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  87. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  88. def set[T](feature: SetFeature[T], value: Set[T]): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  89. def set[T](feature: ArrayFeature[T], value: Array[T]): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  90. final def set(paramPair: ParamPair[_]): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  91. final def set(param: String, value: Any): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  92. final def set[T](param: Param[T], value: T): Word2VecModel.this.type
    Definition Classes
    Params
  93. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  97. final def setDefault(paramPairs: ParamPair[_]*): Word2VecModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def setDefault[T](param: Param[T], value: T): Word2VecModel.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  99. def setDimension(value: Int): Word2VecModel.this.type

    Definition Classes
    HasEmbeddingsProperties
  100. final def setInputCols(value: String*): Word2VecModel.this.type
    Definition Classes
    HasInputAnnotationCols
  101. def setInputCols(value: Array[String]): Word2VecModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  102. def setLazyAnnotator(value: Boolean): Word2VecModel.this.type
    Definition Classes
    CanBeLazy
  103. final def setOutputCol(value: String): Word2VecModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  104. def setParent(parent: Estimator[Word2VecModel]): Word2VecModel
    Definition Classes
    Model
  105. def setStorageRef(value: String): Word2VecModel.this.type
    Definition Classes
    HasStorageRef
  106. def setVectorSize(value: Int): Word2VecModel.this.type

  107. def setWordVectors(value: Map[String, Array[Float]]): Word2VecModel.this.type

  108. val storageRef: Param[String]

    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  109. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  110. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  111. 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
  112. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  113. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  114. 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
  115. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  116. val uid: String
    Definition Classes
    Word2VecModel → Identifiable
  117. 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
  118. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit
    Definition Classes
    HasStorageRef
  119. val vectorSize: ProtectedParam[Int]

    The dimension of codes after transforming from words (> 0) (Default: 100)

  120. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  121. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  122. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  123. val wordVectors: MapFeature[String, Array[Float]]

    Dictionary of words with their vectors

  124. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  125. def wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  126. def wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column
    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  127. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from HasEmbeddingsProperties

Inherited from HasProtectedParams

Inherited from HasStorageRef

Inherited from HasSimpleAnnotate[Word2VecModel]

Inherited from AnnotatorModel[Word2VecModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[Word2VecModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[Word2VecModel]

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