Class/Object

com.johnsnowlabs.nlp.annotators.ws

WordSegmenterModel

Related Docs: object WordSegmenterModel | package ws

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class WordSegmenterModel extends AnnotatorModel[WordSegmenterModel] with HasSimpleAnnotate[WordSegmenterModel] with PerceptronPredictionUtils

WordSegmenter which tokenizes non-english or non-whitespace separated texts.

Many languages are not whitespace separated and their sentences are a concatenation of many symbols, like Korean, Japanese or Chinese. Without understanding the language, splitting the words into their corresponding tokens is impossible. The WordSegmenter is trained to understand these languages and plit them into semantically correct parts.

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

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

val wordSegmenter = WordSegmenterModel.pretrained()
  .setInputCols("document")
  .setOutputCol("words_segmented")

The default model is "wordseg_pku", default language is "zh", if no values are provided. For available pretrained models please see the Models Hub.

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

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.WordSegmenterModel
import org.apache.spark.ml.Pipeline

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

val wordSegmenter = WordSegmenterModel.pretrained()
  .setInputCols("document")
  .setOutputCol("token")

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

val data = Seq("然而,這樣的處理也衍生了一些問題。").toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("token.result").show(false)
+--------------------------------------------------------+
|result                                                  |
+--------------------------------------------------------+
|[然而, ,, 這樣, 的, 處理, 也, 衍生, 了, 一些, 問題, 。    ]|
+--------------------------------------------------------+
Linear Supertypes
Ordering
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Inherited
  1. WordSegmenterModel
  2. PerceptronPredictionUtils
  3. PerceptronUtils
  4. HasSimpleAnnotate
  5. AnnotatorModel
  6. CanBeLazy
  7. RawAnnotator
  8. HasOutputAnnotationCol
  9. HasInputAnnotationCols
  10. HasOutputAnnotatorType
  11. ParamsAndFeaturesWritable
  12. HasFeatures
  13. DefaultParamsWritable
  14. MLWritable
  15. Model
  16. Transformer
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new WordSegmenterModel()

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    Annotator reference id.

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

  2. new WordSegmenterModel(uid: String)

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    uid

    required uid for storing annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

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    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

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    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T

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    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. def annotate(annotations: Seq[Annotation]): Seq[Annotation]

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    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
    WordSegmenterModelHasSimpleAnnotate
  12. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  13. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  14. def buildWordSegments(taggedSentences: Array[TaggedSentence]): Seq[Annotation]

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  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. final def clear(param: Param[_]): WordSegmenterModel.this.type

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    Definition Classes
    Params
  17. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  18. def copy(extra: ParamMap): WordSegmenterModel

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    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  19. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
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    Params
  20. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
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    Params
  21. def dfAnnotate: UserDefinedFunction

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    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. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  23. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  24. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  25. def explainParams(): String

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    Definition Classes
    Params
  26. def extraValidate(structType: StructType): Boolean

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  27. def extraValidateMsg: String

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    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  28. final def extractParamMap(): ParamMap

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    Definition Classes
    Params
  29. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params
  30. val features: ArrayBuffer[Feature[_, _, _]]

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    Definition Classes
    HasFeatures
  31. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  32. def get[T](feature: StructFeature[T]): Option[T]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  33. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  34. def get[T](feature: SetFeature[T]): Option[Set[T]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  35. def get[T](feature: ArrayFeature[T]): Option[Array[T]]

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    Attributes
    protected
    Definition Classes
    HasFeatures
  36. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  37. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  38. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  39. def getInputCols: Array[String]

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    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  40. def getLazyAnnotator: Boolean

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    Definition Classes
    CanBeLazy
  41. def getModel: AveragedPerceptron

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  42. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  43. final def getOutputCol: String

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    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  44. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  45. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params
  46. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params
  47. def hasParent: Boolean

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    Definition Classes
    Model
  48. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  49. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  50. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  51. val inputAnnotatorTypes: Array[String]

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    Input Annotator Types: DOCUMENT

    Input Annotator Types: DOCUMENT

    Definition Classes
    WordSegmenterModelHasInputAnnotationCols
  52. final val inputCols: StringArrayParam

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    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

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    Definition Classes
    Params
  54. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  55. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  56. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  57. val lazyAnnotator: BooleanParam

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    Definition Classes
    CanBeLazy
  58. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  59. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  60. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  61. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  62. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  63. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  64. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  65. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  66. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  67. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  68. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  69. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  70. val model: StructFeature[AveragedPerceptron]

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    POS model

  71. def msgHelper(schema: StructType): String

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    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  72. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  73. final def notify(): Unit

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    Definition Classes
    AnyRef
  74. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  75. def onWrite(path: String, spark: SparkSession): Unit

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    Attributes
    protected
    Definition Classes
    ParamsAndFeaturesWritable
  76. val optionalInputAnnotatorTypes: Array[String]

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    Definition Classes
    HasInputAnnotationCols
  77. val outputAnnotatorType: AnnotatorType

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    Output Annotator Types: TOKEN

    Output Annotator Types: TOKEN

    Definition Classes
    WordSegmenterModelHasOutputAnnotatorType
  78. final val outputCol: Param[String]

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    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  79. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  80. var parent: Estimator[WordSegmenterModel]

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    Definition Classes
    Model
  81. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  82. def set[T](feature: StructFeature[T], value: T): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  83. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  84. def set[T](feature: SetFeature[T], value: Set[T]): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  85. def set[T](feature: ArrayFeature[T], value: Array[T]): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  86. final def set(paramPair: ParamPair[_]): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    Params
  87. final def set(param: String, value: Any): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    Params
  88. final def set[T](param: Param[T], value: T): WordSegmenterModel.this.type

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    Definition Classes
    Params
  89. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  90. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  91. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  92. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): WordSegmenterModel.this.type

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    Attributes
    protected
    Definition Classes
    HasFeatures
  93. final def setDefault(paramPairs: ParamPair[_]*): WordSegmenterModel.this.type

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    Attributes
    protected
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    Params
  94. final def setDefault[T](param: Param[T], value: T): WordSegmenterModel.this.type

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    Attributes
    protected
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    Params
  95. final def setInputCols(value: String*): WordSegmenterModel.this.type

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    Definition Classes
    HasInputAnnotationCols
  96. final def setInputCols(value: Array[String]): WordSegmenterModel.this.type

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    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  97. def setLazyAnnotator(value: Boolean): WordSegmenterModel.this.type

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    Definition Classes
    CanBeLazy
  98. def setModel(targetModel: AveragedPerceptron): WordSegmenterModel.this.type

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  99. final def setOutputCol(value: String): WordSegmenterModel.this.type

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    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  100. def setParent(parent: Estimator[WordSegmenterModel]): WordSegmenterModel

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    Definition Classes
    Model
  101. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  102. def tag(model: AveragedPerceptron, tokenizedSentences: Array[TokenizedSentence]): Array[TaggedSentence]

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    Tags a group of sentences into POS tagged sentences The logic here is to create a sentence context, run through every word and evaluate its context Based on how frequent a context appears around a word, such context is given a score which is used to predict Some words are marked as non ambiguous from the beginning

    Tags a group of sentences into POS tagged sentences The logic here is to create a sentence context, run through every word and evaluate its context Based on how frequent a context appears around a word, such context is given a score which is used to predict Some words are marked as non ambiguous from the beginning

    tokenizedSentences

    Sentence in the form of single word tokens

    returns

    A list of sentences which have every word tagged

    Definition Classes
    PerceptronPredictionUtils
  103. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  104. final def transform(dataset: Dataset[_]): DataFrame

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    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
  105. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  106. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  107. final def transformSchema(schema: StructType): StructType

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    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator → PipelineStage
  108. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  109. val uid: String

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    required uid for storing annotator to disk

    required uid for storing annotator to disk

    Definition Classes
    WordSegmenterModel → Identifiable
  110. def validate(schema: StructType): Boolean

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    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
  111. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  112. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  113. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. def wrapColumnMetadata(col: Column): Column

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  115. def write: MLWriter

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    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable

Inherited from PerceptronPredictionUtils

Inherited from PerceptronUtils

Inherited from CanBeLazy

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[WordSegmenterModel]

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