Class/Object

com.johnsnowlabs.nlp.annotators.seq2seq

MarianTransformer

Related Docs: object MarianTransformer | package seq2seq

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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()

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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 MarianTransformer(uid: String)

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    uid

    required internal uid for saving annotator

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. final def asInstanceOf[T0]: T0

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

    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]

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    Definition Classes
    HasBatchedAnnotate
  14. val batchSize: IntParam

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    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[_]

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    Attributes
    protected
    Definition Classes
    AnnotatorModel
  16. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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

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    Definition Classes
    Params
  18. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  19. val configProtoBytes: IntArrayParam

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    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

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    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

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

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    Attributes
    protected
    Definition Classes
    Params
  23. final def eq(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    Params
  26. def explainParams(): String

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

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  28. def extraValidateMsg: String

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    Override for additional custom schema checks

    Override for additional custom schema checks

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

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

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

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    Definition Classes
    HasFeatures
  32. def finalize(): Unit

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

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

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

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

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

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    Definition Classes
    Params
  38. def getBatchSize: Int

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    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  39. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  40. def getConfigProtoBytes: Option[Array[Byte]]

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  41. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  42. def getIgnoreTokenIds: Array[Int]

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  43. def getInputCols: Array[String]

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    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  44. def getLangId: String

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  45. def getLazyAnnotator: Boolean

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    Definition Classes
    CanBeLazy
  46. def getMaxInputLength: Int

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  47. def getMaxOutputLength: Int

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  48. def getModelIfNotSet: TensorflowMarian

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

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    Definition Classes
    Params
  50. 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
  51. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  52. def getSignatures: Option[Map[String, String]]

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  53. final def hasDefault[T](param: Param[T]): Boolean

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

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    Definition Classes
    Params
  55. def hasParent: Boolean

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    Definition Classes
    Model
  56. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  57. var ignoreTokenIds: IntArrayParam

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

  58. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

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

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    Input Annotator Type: DOCUMENT

    Input Annotator Type: DOCUMENT

    Definition Classes
    MarianTransformerHasInputAnnotationCols
  61. 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
  62. final def isDefined(param: Param[_]): Boolean

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

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

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    Definition Classes
    Params
  65. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  66. var langId: Param[String]

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    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.

  67. val lazyAnnotator: BooleanParam

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    Definition Classes
    CanBeLazy
  68. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  75. def logName: String

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  80. val maxInputLength: IntParam

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

  81. val maxOutputLength: IntParam

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

  82. def msgHelper(schema: StructType): String

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

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

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

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

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  87. val optionalInputAnnotatorTypes: Array[String]

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    Definition Classes
    HasInputAnnotationCols
  88. val outputAnnotatorType: AnnotatorType

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    Output Annotator Type: DOCUMENT

    Output Annotator Type: DOCUMENT

    Definition Classes
    MarianTransformerHasOutputAnnotatorType
  89. final val outputCol: Param[String]

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

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    Definition Classes
    Params
  91. var parent: Estimator[MarianTransformer]

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

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

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

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

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

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

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

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

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    Definition Classes
    Params
  100. def setBatchSize(size: Int): MarianTransformer.this.type

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    Size of every batch.

    Size of every batch.

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

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  102. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): MarianTransformer.this.type

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Params
  107. final def setDefault[T](param: Param[T], value: T): MarianTransformer.this.type

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    Attributes
    protected
    Definition Classes
    Params
  108. def setIgnoreTokenIds(tokenIds: Array[Int]): MarianTransformer.this.type

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  109. final def setInputCols(value: String*): MarianTransformer.this.type

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    Definition Classes
    HasInputAnnotationCols
  110. def setInputCols(value: Array[String]): MarianTransformer.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
  111. def setLangId(lang: String): MarianTransformer.this.type

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  112. def setLazyAnnotator(value: Boolean): MarianTransformer.this.type

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    Definition Classes
    CanBeLazy
  113. def setMaxInputLength(value: Int): MarianTransformer.this.type

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

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

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

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    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  117. def setParent(parent: Estimator[MarianTransformer]): MarianTransformer

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    Definition Classes
    Model
  118. def setSignatures(value: Map[String, String]): MarianTransformer.this.type

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

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  120. val signatures: MapFeature[String, String]

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    It contains TF model signatures for the laded saved model

  121. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  122. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  123. 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
  124. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  126. 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
  127. def transformSchema(schema: StructType, logging: Boolean): StructType

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

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    required internal uid for saving annotator

    required internal uid for saving annotator

    Definition Classes
    MarianTransformer → Identifiable
  129. 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
  130. val vocabulary: StringArrayParam

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    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

  131. final def wait(): Unit

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

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

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

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  135. def write: MLWriter

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    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  136. def writeSentencePieceModel(path: String, spark: SparkSession, spp: SentencePieceWrapper, suffix: String, filename: String): Unit

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    Definition Classes
    WriteSentencePieceModel
  137. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit

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    Definition Classes
    WriteTensorflowModel
  138. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit

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    Definition Classes
    WriteTensorflowModel
  139. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit

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    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