c

com.johnsnowlabs.ml.tensorflow

TensorflowCamemBert

class TensorflowCamemBert extends Serializable

The CamemBERT model was proposed in CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook’s RoBERTa model released in 2019. It is a model trained on 138GB of French text.

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Instance Constructors

  1. new TensorflowCamemBert(tensorflow: TensorflowWrapper, spp: SentencePieceWrapper, configProtoBytes: Option[Array[Byte]] = None, signatures: Option[Map[String, String]] = None)

    tensorflow

    Albert Model wrapper with TensorFlowWrapper

    spp

    Albert SentencePiece model with SentencePieceWrapper

    configProtoBytes

    Configuration for TensorFlow session

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. val _tfCamemBertSignatures: Map[String, String]
  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
    Attributes
    protected[lang]
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    @throws( ... ) @native()
  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: Any): Boolean
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  9. def finalize(): Unit
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    protected[lang]
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    @throws( classOf[java.lang.Throwable] )
  10. final def getClass(): Class[_]
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    @native()
  11. def hashCode(): Int
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    @native()
  12. final def isInstanceOf[T0]: Boolean
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  13. final def ne(arg0: AnyRef): Boolean
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  14. final def notify(): Unit
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    @native()
  15. final def notifyAll(): Unit
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    @native()
  16. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean): Seq[WordpieceEmbeddingsSentence]
  17. def prepareBatchInputs(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]
  18. val spp: SentencePieceWrapper
  19. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  20. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  21. val tensorflow: TensorflowWrapper
  22. def toString(): String
    Definition Classes
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  23. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
  24. final def wait(): Unit
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    @throws( ... )
  25. final def wait(arg0: Long, arg1: Int): Unit
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  26. final def wait(arg0: Long): Unit
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