class BartTransformer extends AnnotatorModel[BartTransformer] with HasBatchedAnnotate[BartTransformer] with ParamsAndFeaturesWritable with WriteTensorflowModel with HasEngine

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer

The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation.

BART is unique in that it is both bidirectional and auto-regressive, meaning that it can generate text both from left-to-right and from right-to-left. This allows it to capture contextual information from both past and future tokens in a sentence,resulting in more accurate and natural language generation.

The model was trained on a large corpus of text data using a combination of unsupervised and supervised learning techniques. It incorporates pretraining and fine-tuning phases, where the model is first trained on a large unlabeled corpus of text, and then fine-tuned on specific downstream tasks.

BART has achieved state-of-the-art performance on a wide range of NLP tasks, including summarization, question-answering, and language translation. Its ability to handle multiple tasks and its high performance on each of these tasks make it a versatile and valuable tool for natural language processing applications.

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

val bart = BartTransformer.pretrained()
  .setInputCols("document")
  .setOutputCol("generation")

The default model is "distilbart_xsum_12_6", if no name is provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see BartTestSpec.

References:

Paper Abstract:

We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new stateof-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance

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.annotators.seq2seq.GPT2Transformer
import org.apache.spark.ml.Pipeline

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

val bart = BartTransformer.pretrained("distilbart_xsum_12_6")
  .setInputCols(Array("documents"))
  .setMinOutputLength(10)
  .setMaxOutputLength(30)
  .setDoSample(true)
  .setTopK(50)
  .setOutputCol("generation")

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

val data = Seq(
  "PG&E stated it scheduled the blackouts in response to forecasts for high winds " +
  "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were " +
  "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
).toDF("text")
val result = pipeline.fit(data).transform(data)

results.select("generation.result").show(truncate = false)
+--------------------------------------------------------------+
|result                                                        |
+--------------------------------------------------------------+
|[Nearly 800 thousand customers were affected by the shutoffs.]|
+--------------------------------------------------------------+
Linear Supertypes
HasEngine, WriteTensorflowModel, HasBatchedAnnotate[BartTransformer], AnnotatorModel[BartTransformer], CanBeLazy, RawAnnotator[BartTransformer], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[BartTransformer], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. BartTransformer
  2. HasEngine
  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
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new BartTransformer()
  2. new BartTransformer(uid: String)

    uid

    required uid for storing annotator to disk

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 in batches that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every batch of input annotations. Not necessary one to one relationship IMPORTANT: !MUST! return sequences of equal lengths !! IMPORTANT: !MUST! return sentences that belong to the same original row !! (challenging)

    Definition Classes
    BartTransformerHasBatchedAnnotate
  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. var beamSize: IntParam

    Beam size for the beam search algorithm (Default: 4)

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

    ConfigProto from tensorflow, serialized into byte array.

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

  21. def copy(extra: ParamMap): BartTransformer

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. val doSample: BooleanParam

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

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  42. def getBeamSize: Int

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

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

  47. def getEngine: String

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

  49. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  50. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  51. def getMaxOutputLength: Int

  52. def getMinOutputLength: Int

  53. def getModelIfNotSet: Bart

  54. def getNoRepeatNgramSize: Int

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

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

  59. def getRepetitionPenalty: Double

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

  61. def getTemperature: Double

  62. def getTopK: Int

  63. def getTopP: Double

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

    A list of token ids which are ignored in the decoder's output (Default: Array())

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

    Input annotator type : DOCUMENT

    Input annotator type : DOCUMENT

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

    max length of the input sequence (Default: 0)

  92. val maxOutputLength: IntParam

    Maximum length of the sequence to be generated (Default: 20)

  93. val merges: MapFeature[(String, String), Int]

    Holding merges.txt coming from RoBERTa model

  94. val minOutputLength: IntParam

    Minimum length of the sequence to be generated (Default: 0)

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

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

  98. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  99. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  100. def onWrite(path: String, spark: SparkSession): Unit
  101. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  102. val outputAnnotatorType: String

    Output annotator type : DOCUMENT

    Output annotator type : DOCUMENT

    Definition Classes
    BartTransformerHasOutputAnnotatorType
  103. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  104. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  105. var parent: Estimator[BartTransformer]
    Definition Classes
    Model
  106. var randomSeed: Option[Long]

    Optional Random seed for the model.

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

  107. 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.

  108. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  109. def set[T](feature: StructFeature[T], value: T): BartTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  110. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): BartTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. def set[T](feature: SetFeature[T], value: Set[T]): BartTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. def set[T](feature: ArrayFeature[T], value: Array[T]): BartTransformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. final def set(paramPair: ParamPair[_]): BartTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  114. final def set(param: String, value: Any): BartTransformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  115. final def set[T](param: Param[T], value: T): BartTransformer.this.type
    Definition Classes
    Params
  116. def setBatchSize(size: Int): BartTransformer.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  117. def setBeamSize(beamNum: Int): BartTransformer.this.type

  118. def setConfigProtoBytes(bytes: Array[Int]): BartTransformer.this.type

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

  126. def setIgnoreTokenIds(tokenIds: Array[Int]): BartTransformer.this.type

  127. final def setInputCols(value: String*): BartTransformer.this.type
    Definition Classes
    HasInputAnnotationCols
  128. def setInputCols(value: Array[String]): BartTransformer.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  129. def setLazyAnnotator(value: Boolean): BartTransformer.this.type
    Definition Classes
    CanBeLazy
  130. def setMaxInputLength(value: Int): BartTransformer.this.type
  131. def setMaxOutputLength(value: Int): BartTransformer.this.type

  132. def setMerges(value: Map[(String, String), Int]): BartTransformer.this.type

  133. def setMinOutputLength(value: Int): BartTransformer.this.type

  134. def setModelIfNotSet(spark: SparkSession, tfWrapper: TensorflowWrapper, useCache: Boolean): BartTransformer.this.type

  135. def setNoRepeatNgramSize(value: Int): BartTransformer.this.type

  136. final def setOutputCol(value: String): BartTransformer.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  137. def setParent(parent: Estimator[BartTransformer]): BartTransformer
    Definition Classes
    Model
  138. def setRandomSeed(value: Long): BartTransformer.this.type

  139. def setRepetitionPenalty(value: Double): BartTransformer.this.type

  140. def setSignatures(value: Map[String, String]): BartTransformer.this.type

  141. def setTask(value: String): BartTransformer.this.type

  142. def setTemperature(value: Double): BartTransformer.this.type

  143. def setTopK(value: Int): BartTransformer.this.type

  144. def setTopP(value: Double): BartTransformer.this.type

  145. def setUseCache(value: Boolean): BartTransformer.this.type
    Attributes
    protected
  146. def setVocabulary(value: Map[String, Int]): BartTransformer.this.type

  147. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  148. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  149. val task: Param[String]

    Set transformer task, e.g.

    Set transformer task, e.g. "summarize:" (Default: "").

  150. val temperature: DoubleParam

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

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

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

  153. 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)

  154. 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
  155. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  156. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  157. 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
  158. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  159. val uid: String
    Definition Classes
    BartTransformer → Identifiable
  160. val useCache: BooleanParam

    Cache internal state of the model to improve performance

  161. 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
  162. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with bpeTokenizer.encode

  163. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  164. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  165. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  166. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  167. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  168. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  169. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  170. 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 RawAnnotator[BartTransformer]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[BartTransformer]

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