Packages

class MarianTransformer extends AnnotatorModel[MarianTransformer] with HasBatchedAnnotate[MarianTransformer] with WriteTensorflowModel with WriteSentencePieceModel

MarianTransformer: Fast Neural Machine Translation

Marian is an efficient, free Neural Machine Translation framework written in pure C++ with minimal dependencies. It is mainly being developed by the Microsoft Translator team. Many academic (most notably the University of Edinburgh and in the past the Adam Mickiewicz University in Poznań) and commercial contributors help with its development. MarianTransformer uses the models trained by MarianNMT.

It is currently the engine behind the Microsoft Translator Neural Machine Translation services and being deployed by many companies, organizations and research projects.

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

val marian = MarianTransformer.pretrained()
  .setInputCols("sentence")
  .setOutputCol("translation")

The default model is "opus_mt_en_fr", 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 MarianTransformerTestSpec.

Sources :

MarianNMT at GitHub

Marian: Fast Neural Machine Translation in C++

Paper Abstract:

We present Marian, an efficient and self-contained Neural Machine Translation framework with an integrated automatic differentiation engine based on dynamic computation graphs. Marian is written entirely in C++. We describe the design of the encoder-decoder framework and demonstrate that a research-friendly toolkit can achieve high training and translation speed.

Note:

This is a very computationally expensive module especially on larger sequence. The use of an accelerator such as GPU is recommended.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotator.SentenceDetectorDLModel
import com.johnsnowlabs.nlp.annotators.seq2seq.MarianTransformer
import org.apache.spark.ml.Pipeline

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

val sentence = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
  .setInputCols("document")
  .setOutputCol("sentence")

val marian = MarianTransformer.pretrained()
  .setInputCols("sentence")
  .setOutputCol("translation")
  .setMaxInputLength(30)

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

val data = Seq("What is the capital of France? We should know this in french.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(translation.result) as result").show(false)
+-------------------------------------+
|result                               |
+-------------------------------------+
|Quelle est la capitale de la France ?|
|On devrait le savoir en français.    |
+-------------------------------------+
Linear Supertypes
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. MarianTransformer
  2. WriteSentencePieceModel
  3. WriteTensorflowModel
  4. HasBatchedAnnotate
  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
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new MarianTransformer()

    Annotator reference id.

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

  2. new MarianTransformer(uid: String)

    uid

    required internal uid for saving annotator

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  12. def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[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

    batchedAnnotations

    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
    MarianTransformerHasBatchedAnnotate
  13. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotate
  14. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

    Definition Classes
    HasBatchedAnnotate
  15. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  16. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  17. final def clear(param: Param[_]): MarianTransformer.this.type
    Definition Classes
    Params
  18. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  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): MarianTransformer

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  39. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  40. def getConfigProtoBytes: Option[Array[Byte]]

  41. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  42. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  43. def getLangId: String

  44. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  45. def getMaxInputLength: Int

  46. def getMaxOutputLength: Int

  47. def getModelIfNotSet: TensorflowMarian

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  50. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  51. def getSignatures: Option[Map[String, String]]

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

    Input Annotator Type: DOCUMENT

    Input Annotator Type: DOCUMENT

    Definition Classes
    MarianTransformerHasInputAnnotationCols
  59. 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
  60. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  61. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  62. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  63. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  64. var langId: Param[String]

    A string representing the target language in the form of >>id<< (id = valid target language ID) (Default: "")

    A string representing the target language in the form of >>id<< (id = valid target language ID) (Default: "")

    langId is only needed if the model generates multi-lingual target language texts. For instance, for a 'en-fr' model this param is not required to be set.

  65. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  66. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  67. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  74. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. val maxInputLength: IntParam

    Controls the maximum length for encoder inputs (source language texts) (Default: 40)

  79. val maxOutputLength: IntParam

    Controls the maximum length for decoder outputs (target language texts) (Default: 40)

  80. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  81. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  82. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  83. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  84. def onWrite(path: String, spark: SparkSession): Unit
  85. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  86. val outputAnnotatorType: AnnotatorType

    Output Annotator Type: DOCUMENT

    Output Annotator Type: DOCUMENT

    Definition Classes
    MarianTransformerHasOutputAnnotatorType
  87. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  88. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  89. var parent: Estimator[MarianTransformer]
    Definition Classes
    Model
  90. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  91. def set[T](feature: StructFeature[T], value: T): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  92. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  93. def set[T](feature: SetFeature[T], value: Set[T]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  94. def set[T](feature: ArrayFeature[T], value: Array[T]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  95. final def set(paramPair: ParamPair[_]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  96. final def set(param: String, value: Any): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  97. final def set[T](param: Param[T], value: T): MarianTransformer.this.type
    Definition Classes
    Params
  98. def setBatchSize(size: Int): MarianTransformer.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  99. def setConfigProtoBytes(bytes: Array[Int]): MarianTransformer.this.type

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

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  108. def setLangId(lang: String): MarianTransformer.this.type

  109. def setLazyAnnotator(value: Boolean): MarianTransformer.this.type
    Definition Classes
    CanBeLazy
  110. def setMaxInputLength(value: Int): MarianTransformer.this.type

  111. def setMaxOutputLength(value: Int): MarianTransformer.this.type

  112. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper, sppSrc: SentencePieceWrapper, sppTrg: SentencePieceWrapper): MarianTransformer.this.type

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

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  114. def setParent(parent: Estimator[MarianTransformer]): MarianTransformer
    Definition Classes
    Model
  115. def setSignatures(value: Map[String, String]): MarianTransformer.this.type

  116. def setVocabulary(value: Array[String]): MarianTransformer.this.type

  117. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  118. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  119. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  120. 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
  121. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  122. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  123. 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
  124. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  125. val uid: String
    Definition Classes
    MarianTransformer → Identifiable
  126. 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
  127. val vocabulary: StringArrayParam

    Vocabulary used to encode and decode piece tokens generated by SentencePiece.

    Vocabulary used to encode and decode piece tokens generated by SentencePiece. This will be set once the model is created and cannot be changed afterwards

  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 writeSentencePieceModel(path: String, spark: SparkSession, spp: SentencePieceWrapper, suffix: String, filename: String): Unit
    Definition Classes
    WriteSentencePieceModel
  134. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  135. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  136. 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 WriteSentencePieceModel

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[MarianTransformer]

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

setParam *

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