class MarianTransformer extends AnnotatorModel[MarianTransformer] with HasBatchedAnnotate[MarianTransformer] with WriteTensorflowModel with WriteOnnxModel with WriteSentencePieceModel with HasEngine with HasProtectedParams

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.

Note that this model only supports inputs up to 512 tokens. If you are working with longer inputs, consider splitting them first. For example, you can use the SentenceDetectorDL annotator to split longer texts into sentences first.

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 Examples 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
HasProtectedParams, HasEngine, WriteSentencePieceModel, WriteOnnxModel, WriteTensorflowModel, HasBatchedAnnotate[MarianTransformer], AnnotatorModel[MarianTransformer], CanBeLazy, RawAnnotator[MarianTransformer], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[MarianTransformer], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. MarianTransformer
  2. HasProtectedParams
  3. HasEngine
  4. WriteSentencePieceModel
  5. WriteOnnxModel
  6. WriteTensorflowModel
  7. HasBatchedAnnotate
  8. AnnotatorModel
  9. CanBeLazy
  10. RawAnnotator
  11. HasOutputAnnotationCol
  12. HasInputAnnotationCols
  13. HasOutputAnnotatorType
  14. ParamsAndFeaturesWritable
  15. HasFeatures
  16. DefaultParamsWritable
  17. MLWritable
  18. Model
  19. Transformer
  20. PipelineStage
  21. Logging
  22. Params
  23. Serializable
  24. Serializable
  25. Identifiable
  26. AnyRef
  27. Any
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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. implicit class ProtectedParam[T] extends Param[T]
    Definition Classes
    HasProtectedParams
  2. 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
  3. 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
    MarianTransformer → HasBatchedAnnotate
  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. val doSample: BooleanParam

    Whether or not to use sampling, use greedy decoding otherwise (Default: false)

  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. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

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

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

  45. def getEngine: String

    Definition Classes
    HasEngine
  46. def getIgnoreTokenIds: Array[Int]

  47. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  48. def getLangId: String

  49. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  50. def getMaxInputLength: Int

  51. def getMaxOutputLength: Int

  52. def getModelIfNotSet: MarianEncoderDecoder

  53. def getNoRepeatNgramSize: Int

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  56. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  57. def getRandomSeed: Option[Long]

  58. def getRepetitionPenalty: Double

  59. def getSignatures: Option[Map[String, String]]

  60. def getTemperature: Double

  61. def getTopK: Int

  62. def getTopP: Double

  63. def getVocabulary: Array[String]

    do not remove or replace with $(vocabulary) due to a bug in some models

  64. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  65. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  66. def hasParent: Boolean
    Definition Classes
    Model
  67. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  68. var ignoreTokenIds: IntArrayParam

    A list of token ids which are ignored in the decoder's output

  69. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  70. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. val inputAnnotatorTypes: Array[String]

    Input Annotator Type: DOCUMENT

    Input Annotator Type: DOCUMENT

    Definition Classes
    MarianTransformer → HasInputAnnotationCols
  72. 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
  73. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  74. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  75. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  76. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  77. 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.

  78. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  79. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  80. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  87. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. val maxInputLength: IntParam

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

  92. val maxOutputLength: IntParam

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

  93. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  94. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  95. val noRepeatNgramSize: IntParam

    If set to int > 0, all ngrams of that size can only occur once (Default: 0)

  96. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  97. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  98. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    MarianTransformer → ParamsAndFeaturesWritable
  99. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  100. val outputAnnotatorType: AnnotatorType

    Output Annotator Type: DOCUMENT

    Output Annotator Type: DOCUMENT

    Definition Classes
    MarianTransformer → HasOutputAnnotatorType
  101. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  102. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  103. var parent: Estimator[MarianTransformer]
    Definition Classes
    Model
  104. var randomSeed: Option[Long]

    Optional Random seed for the model.

    Optional Random seed for the model. Needs to be of type Long.

  105. val repetitionPenalty: DoubleParam

    The parameter for repetition penalty (Default: 1.0).

    The parameter for repetition penalty (Default: 1.0). 1.0 means no penalty. See this paper for more details.

  106. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  107. def set[T](param: ProtectedParam[T], value: T): MarianTransformer.this.type

    Sets the value for a protected Param.

    Sets the value for a protected Param.

    If the parameter was already set, it will not be set again. Default values do not count as a set value and can be overridden.

    T

    Type of the parameter

    param

    Protected parameter to set

    value

    Value for the parameter

    returns

    This object

    Definition Classes
    HasProtectedParams
  108. def set[T](feature: StructFeature[T], value: T): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  110. def set[T](feature: SetFeature[T], value: Set[T]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. def set[T](feature: ArrayFeature[T], value: Array[T]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. final def set(paramPair: ParamPair[_]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  113. final def set(param: String, value: Any): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  114. final def set[T](param: Param[T], value: T): MarianTransformer.this.type
    Definition Classes
    Params
  115. def setBatchSize(size: Int): MarianTransformer.this.type

    Size of every batch.

    Size of every batch.

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

  117. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  118. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  119. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  120. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  121. final def setDefault(paramPairs: ParamPair[_]*): MarianTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  122. final def setDefault[T](param: Param[T], value: T): MarianTransformer.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  123. def setDoSample(value: Boolean): MarianTransformer.this.type

  124. def setIgnoreTokenIds(tokenIds: Array[Int]): MarianTransformer.this.type

  125. final def setInputCols(value: String*): MarianTransformer.this.type
    Definition Classes
    HasInputAnnotationCols
  126. 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
  127. def setLangId(lang: String): MarianTransformer.this.type

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

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

  131. def setModelIfNotSet(spark: SparkSession, encoder: OnnxWrapper, decoder: OnnxWrapper, sppSrc: SentencePieceWrapper, sppTrg: SentencePieceWrapper): MarianTransformer.this.type
  132. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper, sppSrc: SentencePieceWrapper, sppTrg: SentencePieceWrapper): MarianTransformer.this.type

  133. def setNoRepeatNgramSize(value: Int): MarianTransformer.this.type

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

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  135. def setParent(parent: Estimator[MarianTransformer]): MarianTransformer
    Definition Classes
    Model
  136. def setRandomSeed(value: Long): MarianTransformer.this.type

  137. def setRepetitionPenalty(value: Double): MarianTransformer.this.type

  138. def setSignatures(value: Map[String, String]): MarianTransformer.this.type

  139. def setTemperature(value: Double): MarianTransformer.this.type

  140. def setTopK(value: Int): MarianTransformer.this.type

  141. def setTopP(value: Double): MarianTransformer.this.type

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

  143. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  144. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  145. val temperature: DoubleParam

    The value used to module the next token probabilities (Default: 1.0)

  146. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  147. val topK: IntParam

    The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)

  148. val topP: DoubleParam

    If set to float < 1.0, only the most probable tokens with probabilities that add up to topP or higher are kept for generation (Default: 1.0)

  149. 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
  150. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  151. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  152. 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
  153. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  154. val uid: String
    Definition Classes
    MarianTransformer → Identifiable
  155. 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
  156. 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

  157. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  158. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  159. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  160. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  161. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  162. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  163. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String, dataFileSuffix: String = "_data"): Unit
    Definition Classes
    WriteOnnxModel
  164. def writeSentencePieceModel(path: String, spark: SparkSession, spp: SentencePieceWrapper, suffix: String, filename: String): Unit
    Definition Classes
    WriteSentencePieceModel
  165. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  166. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  167. 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 HasProtectedParams

Inherited from HasEngine

Inherited from WriteSentencePieceModel

Inherited from WriteOnnxModel

Inherited from WriteTensorflowModel

Inherited from HasBatchedAnnotate[MarianTransformer]

Inherited from AnnotatorModel[MarianTransformer]

Inherited from CanBeLazy

Inherited from RawAnnotator[MarianTransformer]

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