Packages

class LanguageDetectorDL extends AnnotatorModel[LanguageDetectorDL] with HasSimpleAnnotate[LanguageDetectorDL] with WriteTensorflowModel with HasEngine

Language Identification and Detection by using CNN and RNN architectures in TensorFlow.

LanguageDetectorDL is an annotator that detects the language of documents or sentences depending on the inputCols. The models are trained on large datasets such as Wikipedia and Tatoeba. Depending on the language (how similar the characters are), the LanguageDetectorDL works best with text longer than 140 characters. The output is a language code in Wiki Code style.

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

Val languageDetector = LanguageDetectorDL.pretrained()
  .setInputCols("sentence")
  .setOutputCol("language")

The default model is "ld_wiki_tatoeba_cnn_21", default language is "xx" (meaning multi-lingual), 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 LanguageDetectorDLTestSpec.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.ld.dl.LanguageDetectorDL
import org.apache.spark.ml.Pipeline

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

val languageDetector = LanguageDetectorDL.pretrained()
  .setInputCols("document")
  .setOutputCol("language")

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

val data = Seq(
  "Spark NLP is an open-source text processing library for advanced natural language processing for the Python, Java and Scala programming languages.",
  "Spark NLP est une bibliothèque de traitement de texte open source pour le traitement avancé du langage naturel pour les langages de programmation Python, Java et Scala.",
  "Spark NLP ist eine Open-Source-Textverarbeitungsbibliothek für fortgeschrittene natürliche Sprachverarbeitung für die Programmiersprachen Python, Java und Scala."
).toDF("text")
val result = pipeline.fit(data).transform(data)

result.select("language.result").show(false)
+------+
|result|
+------+
|[en]  |
|[fr]  |
|[de]  |
+------+
Linear Supertypes
HasEngine, WriteTensorflowModel, HasSimpleAnnotate[LanguageDetectorDL], AnnotatorModel[LanguageDetectorDL], CanBeLazy, RawAnnotator[LanguageDetectorDL], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[LanguageDetectorDL], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. LanguageDetectorDL
  2. HasEngine
  3. WriteTensorflowModel
  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 LanguageDetectorDL()
  2. new LanguageDetectorDL(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. val alphabet: MapFeature[String, Int]

    Alphabet used to feed the TensorFlow model for prediction

  12. 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
    LanguageDetectorDLHasSimpleAnnotate
  13. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  14. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  15. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  16. final def clear(param: Param[_]): LanguageDetectorDL.this.type
    Definition Classes
    Params
  17. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  18. val coalesceSentences: BooleanParam

    Output average of sentences instead of one output per sentence (Default: true).

  19. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

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

  20. def copy(extra: ParamMap): LanguageDetectorDL

    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 engine: Param[String]

    This param is set internally once via loadSavedModel.

    This param is set internally once via loadSavedModel. That's why there is no setter

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

  42. def getConfigProtoBytes: Option[Array[Byte]]

  43. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  44. def getEngine: String

    Definition Classes
    HasEngine
  45. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  46. def getLanguage: Array[String]

  47. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  48. def getModelIfNotSet: TensorflowLD

  49. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  50. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

  53. def getThresholdLabel: String

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

    Annotator reference id.

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

    Definition Classes
    LanguageDetectorDLHasInputAnnotationCols
  61. final val inputCols: StringArrayParam

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  62. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  63. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  64. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  65. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  66. val language: MapFeature[String, Int]

    Language used to map prediction to ISO 639-1 language codes

  67. val languages: StringArrayParam

    Languages the model was trained with.

  68. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  69. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  70. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  77. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  82. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  83. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  84. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  85. def onWrite(path: String, spark: SparkSession): Unit
  86. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  87. val outputAnnotatorType: AnnotatorType
  88. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  89. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  90. var parent: Estimator[LanguageDetectorDL]
    Definition Classes
    Model
  91. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  92. def set[T](feature: StructFeature[T], value: T): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  93. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def set[T](feature: SetFeature[T], value: Set[T]): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. def set[T](feature: ArrayFeature[T], value: Array[T]): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  96. final def set(paramPair: ParamPair[_]): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    Params
  97. final def set(param: String, value: Any): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def set[T](param: Param[T], value: T): LanguageDetectorDL.this.type
    Definition Classes
    Params
  99. def setAlphabet(value: Map[String, Int]): LanguageDetectorDL.this.type

  100. def setCoalesceSentences(value: Boolean): LanguageDetectorDL.this.type

  101. def setConfigProtoBytes(bytes: Array[Int]): LanguageDetectorDL.this.type

  102. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  105. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  106. final def setDefault(paramPairs: ParamPair[_]*): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    Params
  107. final def setDefault[T](param: Param[T], value: T): LanguageDetectorDL.this.type
    Attributes
    protected
    Definition Classes
    Params
  108. final def setInputCols(value: String*): LanguageDetectorDL.this.type
    Definition Classes
    HasInputAnnotationCols
  109. def setInputCols(value: Array[String]): LanguageDetectorDL.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  110. def setLanguage(value: Map[String, Int]): LanguageDetectorDL.this.type

  111. def setLazyAnnotator(value: Boolean): LanguageDetectorDL.this.type
    Definition Classes
    CanBeLazy
  112. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper): LanguageDetectorDL.this.type

  113. final def setOutputCol(value: String): LanguageDetectorDL.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  114. def setParent(parent: Estimator[LanguageDetectorDL]): LanguageDetectorDL
    Definition Classes
    Model
  115. def setThreshold(threshold: Float): LanguageDetectorDL.this.type

  116. def setThresholdLabel(label: String): LanguageDetectorDL.this.type

  117. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  118. val threshold: FloatParam

    The minimum threshold for the final result, otherwise it will be either "unk" or the value set in thresholdLabel (Default: 0.1f).

    The minimum threshold for the final result, otherwise it will be either "unk" or the value set in thresholdLabel (Default: 0.1f). Value is between 0.0 to 1.0. Try to set this lower if your text is hard to predict

  119. val thresholdLabel: Param[String]

    Value for the classification, if confidence is less than threshold (Default: "unk").

  120. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  121. 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
  122. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  123. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  124. 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
  125. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  126. val uid: String
    Definition Classes
    LanguageDetectorDL → Identifiable
  127. 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
  128. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  129. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  130. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  131. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  132. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  133. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  134. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  135. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from HasEngine

Inherited from WriteTensorflowModel

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[LanguageDetectorDL]

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.

Members

Parameter setters

Parameter getters