Class

com.johnsnowlabs.ml.tensorflow

TensorflowXlmRoBertaClassification

Related Doc: package tensorflow

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class TensorflowXlmRoBertaClassification extends Serializable

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Instance Constructors

  1. new TensorflowXlmRoBertaClassification(tensorflowWrapper: TensorflowWrapper, spp: SentencePieceWrapper, configProtoBytes: Option[Array[Byte]] = None, tags: Map[String, Int], signatures: Option[Map[String, String]] = None)

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    tensorflowWrapper

    XLM-RoBERTa Model wrapper with TensorFlow Wrapper

    spp

    XlmRoberta SentencePiece model with SentencePieceWrapper

    configProtoBytes

    Configuration for TensorFlow session

    tags

    labels which model was trained with in order

    signatures

    TF v2 signatures in Spark NLP

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 _tfXlmRoBertaSignatures: Map[String, String]

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  5. final def asInstanceOf[T0]: T0

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  6. def calcluateSoftmax(scores: Array[Float]): Array[Float]

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  7. def clone(): AnyRef

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  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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  11. final def getClass(): Class[_]

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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  14. final def ne(arg0: AnyRef): Boolean

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean): Seq[Annotation]

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  18. def prepareBatchInputs(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]

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    Encode the input sequence to indexes IDs adding padding where necessary

  19. val spp: SentencePieceWrapper

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    XlmRoberta SentencePiece model with SentencePieceWrapper

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

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  21. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]

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  22. val tensorflowWrapper: TensorflowWrapper

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    XLM-RoBERTa Model wrapper with TensorFlow Wrapper

  23. def toString(): String

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  24. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]

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  25. final def wait(): Unit

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  26. final def wait(arg0: Long, arg1: Int): Unit

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  27. final def wait(arg0: Long): Unit

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