Packages

class ChunkEmbeddings extends AnnotatorModel[ChunkEmbeddings] with HasSimpleAnnotate[ChunkEmbeddings]

This annotator utilizes WordEmbeddings, BertEmbeddings etc. to generate chunk embeddings from either Chunker, NGramGenerator, or NerConverter outputs.

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

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.sbd.pragmatic.SentenceDetector
import com.johnsnowlabs.nlp.annotators.{NGramGenerator, Tokenizer}
import com.johnsnowlabs.nlp.embeddings.WordEmbeddingsModel
import com.johnsnowlabs.nlp.embeddings.ChunkEmbeddings
import org.apache.spark.ml.Pipeline

// Extract the Embeddings from the NGrams
val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

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

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

val nGrams = new NGramGenerator()
  .setInputCols("token")
  .setOutputCol("chunk")
  .setN(2)

val embeddings = WordEmbeddingsModel.pretrained()
  .setInputCols("sentence", "token")
  .setOutputCol("embeddings")
  .setCaseSensitive(false)

// Convert the NGram chunks into Word Embeddings
val chunkEmbeddings = new ChunkEmbeddings()
  .setInputCols("chunk", "embeddings")
  .setOutputCol("chunk_embeddings")
  .setPoolingStrategy("AVERAGE")

val pipeline = new Pipeline()
  .setStages(Array(
    documentAssembler,
    sentence,
    tokenizer,
    nGrams,
    embeddings,
    chunkEmbeddings
  ))

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

result.selectExpr("explode(chunk_embeddings) as result")
  .select("result.annotatorType", "result.result", "result.embeddings")
  .show(5, 80)
+---------------+----------+--------------------------------------------------------------------------------+
|  annotatorType|    result|                                                                      embeddings|
+---------------+----------+--------------------------------------------------------------------------------+
|word_embeddings|   This is|[-0.55661, 0.42829502, 0.86661, -0.409785, 0.06316501, 0.120775, -0.0732005, ...|
|word_embeddings|      is a|[-0.40674996, 0.22938299, 0.50597, -0.288195, 0.555655, 0.465145, 0.140118, 0...|
|word_embeddings|a sentence|[0.17417, 0.095253006, -0.0530925, -0.218465, 0.714395, 0.79860497, 0.0129999...|
|word_embeddings|sentence .|[0.139705, 0.177955, 0.1887775, -0.45545, 0.20030999, 0.461557, -0.07891501, ...|
+---------------+----------+--------------------------------------------------------------------------------+
Linear Supertypes
HasSimpleAnnotate[ChunkEmbeddings], AnnotatorModel[ChunkEmbeddings], CanBeLazy, RawAnnotator[ChunkEmbeddings], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[ChunkEmbeddings], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. ChunkEmbeddings
  2. HasSimpleAnnotate
  3. AnnotatorModel
  4. CanBeLazy
  5. RawAnnotator
  6. HasOutputAnnotationCol
  7. HasInputAnnotationCols
  8. HasOutputAnnotatorType
  9. ParamsAndFeaturesWritable
  10. HasFeatures
  11. DefaultParamsWritable
  12. MLWritable
  13. Model
  14. Transformer
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new ChunkEmbeddings()

    Internal constructor to submit a random UID

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

    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. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  20. 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
  21. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  22. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  23. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  24. def explainParams(): String
    Definition Classes
    Params
  25. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  26. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  39. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  40. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  41. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  42. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  43. def getPoolingStrategy: String

    Choose how you would like to aggregate Word Embeddings to Chunk Embeddings: AVERAGE or SUM

  44. def getSkipOOV: Boolean

    Whether to discard default vectors for OOV words from the aggregation / pooling

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

    Input annotator type : CHUNK, WORD_EMBEDDINGS

    Input annotator type : CHUNK, WORD_EMBEDDINGS

    Definition Classes
    ChunkEmbeddings → HasInputAnnotationCols
  52. 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
  53. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  54. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  55. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  56. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  57. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  58. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  59. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  66. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  71. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  72. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  73. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  74. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  75. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  76. val outputAnnotatorType: AnnotatorType

    Output annotator type : WORD_EMBEDDINGS

    Output annotator type : WORD_EMBEDDINGS

    Definition Classes
    ChunkEmbeddings → HasOutputAnnotatorType
  77. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  78. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  79. var parent: Estimator[ChunkEmbeddings]
    Definition Classes
    Model
  80. val poolingStrategy: Param[String]

    Choose how you would like to aggregate Word Embeddings to Chunk Embeddings: "AVERAGE" or "SUM" (Default: "AVERAGE")

  81. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  82. def set[T](feature: StructFeature[T], value: T): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  83. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  84. def set[T](feature: SetFeature[T], value: Set[T]): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  85. def set[T](feature: ArrayFeature[T], value: Array[T]): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  86. final def set(paramPair: ParamPair[_]): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  87. final def set(param: String, value: Any): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  88. final def set[T](param: Param[T], value: T): ChunkEmbeddings.this.type
    Definition Classes
    Params
  89. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  90. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  91. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  92. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  93. final def setDefault(paramPairs: ParamPair[_]*): ChunkEmbeddings.this.type
    Attributes
    protected
    Definition Classes
    Params
  94. final def setDefault[T](param: Param[T], value: T): ChunkEmbeddings.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  95. final def setInputCols(value: String*): ChunkEmbeddings.this.type
    Definition Classes
    HasInputAnnotationCols
  96. def setInputCols(value: Array[String]): ChunkEmbeddings.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  97. def setLazyAnnotator(value: Boolean): ChunkEmbeddings.this.type
    Definition Classes
    CanBeLazy
  98. final def setOutputCol(value: String): ChunkEmbeddings.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  99. def setParent(parent: Estimator[ChunkEmbeddings]): ChunkEmbeddings
    Definition Classes
    Model
  100. def setPoolingStrategy(strategy: String): ChunkEmbeddings.this.type

    PoolingStrategy must be either AVERAGE or SUM

  101. def setSkipOOV(value: Boolean): ChunkEmbeddings.this.type

    Whether to discard default vectors for OOV words from the aggregation / pooling

  102. val skipOOV: BooleanParam

    Whether to discard default vectors for OOV words from the aggregation / pooling (Default: true)

  103. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  104. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  105. 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
  106. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  107. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  108. 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
  109. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  110. val uid: String
    Definition Classes
    ChunkEmbeddings → Identifiable
  111. 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
  112. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  113. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  115. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  116. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from HasSimpleAnnotate[ChunkEmbeddings]

Inherited from AnnotatorModel[ChunkEmbeddings]

Inherited from CanBeLazy

Inherited from RawAnnotator[ChunkEmbeddings]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[ChunkEmbeddings]

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