Class/Object

com.johnsnowlabs.nlp.embeddings

RoBertaEmbeddings

Related Docs: object RoBertaEmbeddings | package embeddings

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class RoBertaEmbeddings extends AnnotatorModel[RoBertaEmbeddings] with HasBatchedAnnotate[RoBertaEmbeddings] with WriteTensorflowModel with HasEmbeddingsProperties with HasStorageRef with HasCaseSensitiveProperties

The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018.

It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with much larger mini-batches and learning rates.

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

val embeddings = RoBertaEmbeddings.pretrained()
  .setInputCols("document", "token")
  .setOutputCol("embeddings")

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

For extended examples of usage, see the Spark NLP Workshop and the RoBertaEmbeddingsTestSpec. Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them.

Paper Abstract:

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.

Tips:

The original code can be found here https://github.com/pytorch/fairseq/tree/master/examples/roberta.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.Tokenizer
import com.johnsnowlabs.nlp.embeddings.RoBertaEmbeddings
import com.johnsnowlabs.nlp.EmbeddingsFinisher
import org.apache.spark.ml.Pipeline

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

val tokenizer = new Tokenizer()
  .setInputCols(Array("document"))
  .setOutputCol("token")

val embeddings = RoBertaEmbeddings.pretrained()
  .setInputCols("document", "token")
  .setOutputCol("embeddings")
  .setCaseSensitive(true)

val embeddingsFinisher = new EmbeddingsFinisher()
  .setInputCols("embeddings")
  .setOutputCols("finished_embeddings")
  .setOutputAsVector(true)
  .setCleanAnnotations(false)

val pipeline = new Pipeline()
  .setStages(Array(
    documentAssembler,
    tokenizer,
    embeddings,
    embeddingsFinisher
  ))

val data = Seq("This is a sentence.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(finished_embeddings) as result").show(5, 80)
+--------------------------------------------------------------------------------+
|                                                                          result|
+--------------------------------------------------------------------------------+
|[0.18792399764060974,-0.14591649174690247,0.20547787845134735,0.1468472778797...|
|[0.22845706343650818,0.18073144555091858,0.09725798666477203,-0.0417917296290...|
|[0.07037967443466187,-0.14801117777824402,-0.03603338822722435,-0.17893412709...|
|[-0.08734266459941864,0.2486150562763214,-0.009067727252840996,-0.24408400058...|
|[0.22409197688102722,-0.4312366545200348,0.1401449590921402,0.356410235166549...|
+--------------------------------------------------------------------------------+
See also

Annotators Main Page for a list of transformer based embeddings

Linear Supertypes
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. RoBertaEmbeddings
  2. HasCaseSensitiveProperties
  3. HasStorageRef
  4. HasEmbeddingsProperties
  5. WriteTensorflowModel
  6. HasBatchedAnnotate
  7. AnnotatorModel
  8. CanBeLazy
  9. RawAnnotator
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. HasOutputAnnotatorType
  13. ParamsAndFeaturesWritable
  14. HasFeatures
  15. DefaultParamsWritable
  16. MLWritable
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new RoBertaEmbeddings()

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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 RoBertaEmbeddings(uid: String)

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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
    RoBertaEmbeddings → 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
    RoBertaEmbeddings → HasBatchedAnnotate
  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. val caseSensitive: BooleanParam

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    Whether to ignore case in index lookups (Default depends on model)

    Whether to ignore case in index lookups (Default depends on model)

    Definition Classes
    HasCaseSensitiveProperties
  17. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean

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

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    Definition Classes
    Params
  19. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  20. 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()

  21. def copy(extra: ParamMap): RoBertaEmbeddings

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    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

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    Attributes
    protected
    Definition Classes
    Params
  23. def createDatabaseConnection(database: Name): RocksDBConnection

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    Definition Classes
    HasStorageRef
  24. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  25. val dimension: IntParam

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    Number of embedding dimensions (Default depends on model)

    Number of embedding dimensions (Default depends on model)

    Definition Classes
    HasEmbeddingsProperties
  26. final def eq(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    Params
  29. def explainParams(): String

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

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  31. def extraValidateMsg: String

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    Override for additional custom schema checks

    Override for additional custom schema checks

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

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

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

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    Definition Classes
    HasFeatures
  35. def finalize(): Unit

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

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

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

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

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

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    Definition Classes
    Params
  41. def getBatchSize: Int

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    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  42. def getCaseSensitive: Boolean

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    Definition Classes
    HasCaseSensitiveProperties
  43. final def getClass(): Class[_]

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

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

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    Definition Classes
    Params
  46. def getDimension: Int

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    Definition Classes
    HasEmbeddingsProperties
  47. def getInputCols: Array[String]

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    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  48. def getLazyAnnotator: Boolean

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    Definition Classes
    CanBeLazy
  49. def getMaxSentenceLength: Int

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  50. def getModelIfNotSet: TensorflowRoBerta

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

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    Definition Classes
    Params
  52. 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
  53. def getParam(paramName: String): Param[Any]

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

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  55. def getStorageRef: String

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    Definition Classes
    HasStorageRef
  56. final def hasDefault[T](param: Param[T]): Boolean

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

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    Definition Classes
    Params
  58. def hasParent: Boolean

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    Definition Classes
    Model
  59. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  60. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

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

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    Input Annotator Types: DOCUMENT, TOKEN

    Input Annotator Types: DOCUMENT, TOKEN

    Definition Classes
    RoBertaEmbeddings → HasInputAnnotationCols
  63. 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
  64. final def isDefined(param: Param[_]): Boolean

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

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

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    Definition Classes
    Params
  67. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  68. val lazyAnnotator: BooleanParam

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    Definition Classes
    CanBeLazy
  69. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  76. def logName: String

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  81. val maxSentenceLength: IntParam

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    Max sentence length to process (Default: 128)

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

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    Holding merges.txt coming from RoBERTa model

  83. def msgHelper(schema: StructType): String

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

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

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

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

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    Definition Classes
    RoBertaEmbeddings → ParamsAndFeaturesWritable
  88. val outputAnnotatorType: AnnotatorType

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    Output Annotator Types: WORD_EMBEDDINGS

    Output Annotator Types: WORD_EMBEDDINGS

    Definition Classes
    RoBertaEmbeddings → HasOutputAnnotatorType
  89. final val outputCol: Param[String]

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    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  90. def padTokenId: Int

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  91. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  92. var parent: Estimator[RoBertaEmbeddings]

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

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  94. def sentenceEndTokenId: Int

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  95. def sentenceStartTokenId: Int

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  96. def set[T](feature: StructFeature[T], value: T): RoBertaEmbeddings.this.type

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

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

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

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

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

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

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    Definition Classes
    Params
  103. def setBatchSize(size: Int): RoBertaEmbeddings.this.type

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    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  104. def setCaseSensitive(value: Boolean): RoBertaEmbeddings.this.type

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    Whether to lowercase tokens or not

    Whether to lowercase tokens or not

    Definition Classes
    RoBertaEmbeddings → HasCaseSensitiveProperties
  105. def setConfigProtoBytes(bytes: Array[Int]): RoBertaEmbeddings.this.type

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Params
  112. def setDimension(value: Int): RoBertaEmbeddings.this.type

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    Set Embeddings dimensions for the RoBERTa model.

    Set Embeddings dimensions for the RoBERTa model. Only possible to set this when the first time is saved dimension is not changeable, it comes from RoBERTa config file.

    Definition Classes
    RoBertaEmbeddings → HasEmbeddingsProperties
  113. final def setInputCols(value: String*): RoBertaEmbeddings.this.type

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    Definition Classes
    HasInputAnnotationCols
  114. final def setInputCols(value: Array[String]): RoBertaEmbeddings.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
  115. def setLazyAnnotator(value: Boolean): RoBertaEmbeddings.this.type

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    Definition Classes
    CanBeLazy
  116. def setMaxSentenceLength(value: Int): RoBertaEmbeddings.this.type

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  117. def setMerges(value: Map[(String, String), Int]): RoBertaEmbeddings.this.type

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  118. def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: TensorflowWrapper): RoBertaEmbeddings

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  119. final def setOutputCol(value: String): RoBertaEmbeddings.this.type

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    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  120. def setParent(parent: Estimator[RoBertaEmbeddings]): RoBertaEmbeddings

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

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  122. def setStorageRef(value: String): RoBertaEmbeddings.this.type

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    Definition Classes
    HasStorageRef
  123. def setVocabulary(value: Map[String, Int]): RoBertaEmbeddings.this.type

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  124. val signatures: MapFeature[String, String]

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    It contains TF model signatures for the laded saved model

  125. val storageRef: Param[String]

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    Unique identifier for storage (Default: this.uid)

    Unique identifier for storage (Default: this.uid)

    Definition Classes
    HasStorageRef
  126. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  127. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  128. def tokenizeWithAlignment(tokens: Seq[TokenizedSentence]): Seq[WordpieceTokenizedSentence]

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  129. 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
  130. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  132. 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
  133. def transformSchema(schema: StructType, logging: Boolean): StructType

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

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    Definition Classes
    RoBertaEmbeddings → Identifiable
  135. 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
  136. def validateStorageRef(dataset: Dataset[_], inputCols: Array[String], annotatorType: String): Unit

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    Definition Classes
    HasStorageRef
  137. val vocabulary: MapFeature[String, Int]

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

  138. final def wait(): Unit

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

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

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

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    Attributes
    protected
    Definition Classes
    RawAnnotator
  142. def wrapEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column

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    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  143. def wrapSentenceEmbeddingsMetadata(col: Column, embeddingsDim: Int, embeddingsRef: Option[String] = None): Column

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    Attributes
    protected
    Definition Classes
    HasEmbeddingsProperties
  144. def write: MLWriter

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    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  145. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit

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    Definition Classes
    WriteTensorflowModel
  146. 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
  147. 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 HasStorageRef

Inherited from HasEmbeddingsProperties

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[RoBertaEmbeddings]

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.

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters