Packages

class NorvigSweetingModel extends AnnotatorModel[NorvigSweetingModel] with HasSimpleAnnotate[NorvigSweetingModel] with NorvigSweetingParams

This annotator retrieves tokens and makes corrections automatically if not found in an English dictionary. Inspired by Norvig model and SymSpell.

The Symmetric Delete spelling correction algorithm reduces the complexity of edit candidate generation and dictionary lookup for a given Damerau-Levenshtein distance. It is six orders of magnitude faster (than the standard approach with deletes + transposes + replaces + inserts) and language independent.

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

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

val spellChecker = NorvigSweetingModel.pretrained()
  .setInputCols("token")
  .setOutputCol("spell")
  .setDoubleVariants(true)

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

For extended examples of usage, see the NorvigSweetingTestSpec.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.annotators.spell.norvig.NorvigSweetingModel

import org.apache.spark.ml.Pipeline

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

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

val spellChecker = NorvigSweetingModel.pretrained()
  .setInputCols("token")
  .setOutputCol("spell")

val pipeline = new Pipeline().setStages(Array(
  documentAssembler,
  tokenizer,
  spellChecker
))

val data = Seq("somtimes i wrrite wordz erong.").toDF("text")
val result = pipeline.fit(data).transform(data)
result.select("spell.result").show(false)
+--------------------------------------+
|result                                |
+--------------------------------------+
|[sometimes, i, write, words, wrong, .]|
+--------------------------------------+
See also

SymmetricDeleteModel for an alternative approach to spell checking

ContextSpellCheckerModel for a DL based approach

Linear Supertypes
NorvigSweetingParams, HasSimpleAnnotate[NorvigSweetingModel], AnnotatorModel[NorvigSweetingModel], CanBeLazy, RawAnnotator[NorvigSweetingModel], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[NorvigSweetingModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. NorvigSweetingModel
  2. NorvigSweetingParams
  3. HasSimpleAnnotate
  4. AnnotatorModel
  5. CanBeLazy
  6. RawAnnotator
  7. HasOutputAnnotationCol
  8. HasInputAnnotationCols
  9. HasOutputAnnotatorType
  10. ParamsAndFeaturesWritable
  11. HasFeatures
  12. DefaultParamsWritable
  13. MLWritable
  14. Model
  15. Transformer
  16. PipelineStage
  17. Logging
  18. Params
  19. Serializable
  20. Serializable
  21. Identifiable
  22. AnyRef
  23. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new NorvigSweetingModel()

    Annotator reference id.

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

  2. new NorvigSweetingModel(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
    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
    NorvigSweetingModel → HasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  14. val caseSensitive: BooleanParam

    Sensitivity on spell checking (Default: true).

    Sensitivity on spell checking (Default: true). Might affect accuracy

    Definition Classes
    NorvigSweetingParams
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. def checkSpellWord(raw: String): (String, Double)
  17. final def clear(param: Param[_]): NorvigSweetingModel.this.type
    Definition Classes
    Params
  18. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  19. def computeDoubleVariants(word: String): List[String]

    variants of variants of a word

  20. def copy(extra: ParamMap): NorvigSweetingModel

    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. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. 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
  24. val doubleVariants: BooleanParam

    Increase search at cost of performance (Default: false).

    Increase search at cost of performance (Default: false). Enables extra check for word combinations, More accuracy at performance

    Definition Classes
    NorvigSweetingParams
  25. val dupsLimit: IntParam

    Maximum duplicate of characters in a word to consider (Default: 2).

    Maximum duplicate of characters in a word to consider (Default: 2). Maximum duplicate of characters to account for.

    Definition Classes
    NorvigSweetingParams
  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. val frequencyPriority: BooleanParam

    Applies frequency over hamming in intersections (Default: true).

    Applies frequency over hamming in intersections (Default: true). When false hamming takes priority

    Definition Classes
    NorvigSweetingParams
  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 getCaseSensitive: Boolean

    Sensitivity on spell checking (Default: true).

    Sensitivity on spell checking (Default: true). Might affect accuracy

    Definition Classes
    NorvigSweetingParams
  43. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  44. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  45. def getDoubleVariants: Boolean

    Increase search at cost of performance (Default: false).

    Increase search at cost of performance (Default: false). Enables extra check for word combinations

    Definition Classes
    NorvigSweetingParams
  46. def getDupsLimit: Int

    Maximum duplicate of characters in a word to consider (Default: 2).

    Maximum duplicate of characters in a word to consider (Default: 2). Maximum duplicate of characters to account for.

    Definition Classes
    NorvigSweetingParams
  47. def getFrequencyOrHammingRecommendation(wordsByFrequency: List[(String, Long)], wordsByHamming: List[(String, Long)], input: String): (Option[String], Double)
  48. def getFrequencyPriority: Boolean

    Applies frequency over hamming in intersections (Default: true).

    Applies frequency over hamming in intersections (Default: true). When false hamming takes priority

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  50. def getIntersections: Int

    Hamming intersections to attempt (Default: 10).

    Hamming intersections to attempt (Default: 10).

    Definition Classes
    NorvigSweetingParams
  51. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  52. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  53. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  54. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  55. def getReductLimit: Int

    Word reduction limit (Default: 3).

    Word reduction limit (Default: 3).

    Definition Classes
    NorvigSweetingParams
  56. def getResult(wordsByFrequency: List[(String, Long)], wordsByHamming: List[(String, Long)], input: String): (String, Double)
  57. def getResultByFrequency(wordsByFrequency: List[(String, Long)]): (Option[String], Double)
  58. def getResultByHamming(wordsByHamming: List[(String, Long)]): (Option[String], Double)
  59. def getScoreFrequency(word: String): Double
  60. def getShortCircuit: Boolean

    Increase performance at cost of accuracy (Default: false).

    Increase performance at cost of accuracy (Default: false). Faster but less accurate mode

    Definition Classes
    NorvigSweetingParams
  61. def getSortedWordsByFrequency(words: List[String], input: String): List[(String, Long)]
  62. def getSortedWordsByHamming(words: List[String], input: String): List[(String, Long)]
  63. def getVowelSwapLimit: Int

    Vowel swap attempts (Default: 6).

    Vowel swap attempts (Default: 6).

    Definition Classes
    NorvigSweetingParams
  64. def getWordCount: Map[String, Long]

    Attributes
    protected
  65. def getWordSizeIgnore: Int

    Minimum size of word before ignoring (Default: 3).

    Minimum size of word before ignoring (Default: 3). Minimum size of word before moving on.

    Definition Classes
    NorvigSweetingParams
  66. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  67. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  68. def hasParent: Boolean
    Definition Classes
    Model
  69. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  70. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  71. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : TOKEN

    Input annotator type : TOKEN

    Definition Classes
    NorvigSweetingModel → HasInputAnnotationCols
  73. 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
  74. val intersections: IntParam

    Hamming intersections to attempt (Default: 10).

    Hamming intersections to attempt (Default: 10).

    Definition Classes
    NorvigSweetingParams
  75. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  76. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  77. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  78. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  79. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  80. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  81. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  88. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  93. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  94. def normalizeFrequencyValue(value: Long): Double
  95. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  96. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  97. def onWrite(path: String, spark: SparkSession): Unit
    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  98. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  99. val outputAnnotatorType: AnnotatorType

    Output annotator type : TOKEN

    Output annotator type : TOKEN

    Definition Classes
    NorvigSweetingModel → HasOutputAnnotatorType
  100. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  101. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  102. var parent: Estimator[NorvigSweetingModel]
    Definition Classes
    Model
  103. val reductLimit: IntParam

    Word reduction limit (Default: 3).

    Word reduction limit (Default: 3).

    Definition Classes
    NorvigSweetingParams
  104. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  105. def set[T](feature: StructFeature[T], value: T): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  106. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  107. def set[T](feature: SetFeature[T], value: Set[T]): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  108. def set[T](feature: ArrayFeature[T], value: Array[T]): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. final def set(paramPair: ParamPair[_]): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  110. final def set(param: String, value: Any): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  111. final def set[T](param: Param[T], value: T): NorvigSweetingModel.this.type
    Definition Classes
    Params
  112. def setCaseSensitive(value: Boolean): NorvigSweetingModel.this.type

    Sensitivity on spell checking (Default: true).

    Sensitivity on spell checking (Default: true). Might affect accuracy

    Definition Classes
    NorvigSweetingParams
  113. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  114. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  115. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  116. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  117. final def setDefault(paramPairs: ParamPair[_]*): NorvigSweetingModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  118. final def setDefault[T](param: Param[T], value: T): NorvigSweetingModel.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  119. def setDoubleVariants(value: Boolean): NorvigSweetingModel.this.type

    Increase search at cost of performance (Default: false).

    Increase search at cost of performance (Default: false). Enables extra check for word combinations

    Definition Classes
    NorvigSweetingParams
  120. def setDupsLimit(value: Int): NorvigSweetingModel.this.type

    Maximum duplicate of characters in a word to consider (Default: 2).

    Maximum duplicate of characters in a word to consider (Default: 2). Maximum duplicate of characters to account for. Defaults to 2.

    Definition Classes
    NorvigSweetingParams
  121. def setFrequencyPriority(value: Boolean): NorvigSweetingModel.this.type

    Applies frequency over hamming in intersections (Default: true).

    Applies frequency over hamming in intersections (Default: true). When false hamming takes priority

    Definition Classes
    NorvigSweetingParams
  122. final def setInputCols(value: String*): NorvigSweetingModel.this.type
    Definition Classes
    HasInputAnnotationCols
  123. def setInputCols(value: Array[String]): NorvigSweetingModel.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  124. def setIntersections(value: Int): NorvigSweetingModel.this.type

    Hamming intersections to attempt (Default: 10).

    Hamming intersections to attempt (Default: 10).

    Definition Classes
    NorvigSweetingParams
  125. def setLazyAnnotator(value: Boolean): NorvigSweetingModel.this.type
    Definition Classes
    CanBeLazy
  126. final def setOutputCol(value: String): NorvigSweetingModel.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  127. def setParent(parent: Estimator[NorvigSweetingModel]): NorvigSweetingModel
    Definition Classes
    Model
  128. def setReductLimit(value: Int): NorvigSweetingModel.this.type

    Word reduction limit (Default: 3).

    Word reduction limit (Default: 3).

    Definition Classes
    NorvigSweetingParams
  129. def setShortCircuit(value: Boolean): NorvigSweetingModel.this.type

    Increase performance at cost of accuracy (Default: false).

    Increase performance at cost of accuracy (Default: false). Faster but less accurate mode

    Definition Classes
    NorvigSweetingParams
  130. def setVowelSwapLimit(value: Int): NorvigSweetingModel.this.type

    Vowel swap attempts (Default: 6).

    Vowel swap attempts (Default: 6).

    Definition Classes
    NorvigSweetingParams
  131. def setWordCount(value: Map[String, Long]): NorvigSweetingModel.this.type

  132. def setWordSizeIgnore(value: Int): NorvigSweetingModel.this.type

    Minimum size of word before ignoring (Default: 3).

    Minimum size of word before ignoring (Default: 3). Minimum size of word before moving on.

    Definition Classes
    NorvigSweetingParams
  133. val shortCircuit: BooleanParam

    Increase performance at cost of accuracy (Default: false).

    Increase performance at cost of accuracy (Default: false). Faster but less accurate mode

    Definition Classes
    NorvigSweetingParams
  134. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  135. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  136. 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
  137. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  138. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  139. 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
  140. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  141. val uid: String
    Definition Classes
    NorvigSweetingModel → Identifiable
  142. 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
  143. val vowelSwapLimit: IntParam

    Vowel swap attempts (Default: 6).

    Vowel swap attempts (Default: 6).

    Definition Classes
    NorvigSweetingParams
  144. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  145. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  146. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  147. val wordCount: MapFeature[String, Long]

    Number of words in the dictionary

    Number of words in the dictionary

    Attributes
    protected
  148. val wordSizeIgnore: IntParam

    Minimum size of word before ignoring (Default: 3).

    Minimum size of word before ignoring (Default: 3). Minimum size of word before moving on.

    Definition Classes
    NorvigSweetingParams
  149. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  150. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from NorvigSweetingParams

Inherited from HasSimpleAnnotate[NorvigSweetingModel]

Inherited from AnnotatorModel[NorvigSweetingModel]

Inherited from CanBeLazy

Inherited from RawAnnotator[NorvigSweetingModel]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[NorvigSweetingModel]

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