Class

com.johnsnowlabs.ml.tensorflow

TensorflowRoBerta

Related Doc: package tensorflow

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class TensorflowRoBerta extends Serializable

TensorFlow backend for RoBERTa and Longformer

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Visibility
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Instance Constructors

  1. new TensorflowRoBerta(tensorflowWrapper: TensorflowWrapper, sentenceStartTokenId: Int, sentenceEndTokenId: Int, padTokenId: Int, configProtoBytes: Option[Array[Byte]] = None, signatures: Option[Map[String, String]] = None)

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    tensorflowWrapper

    tensorflowWrapper class

    sentenceStartTokenId

    special token id for <s>

    sentenceEndTokenId

    special token id for </s>

    configProtoBytes

    ProtoBytes for TensorFlow session config

    signatures

    Model's inputs and output(s) signatures

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 _tfRoBertaSignatures: Map[String, String]

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  5. final def asInstanceOf[T0]: T0

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  6. def calculateEmbeddings(sentences: Seq[WordpieceTokenizedSentence], originalTokenSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean): Seq[WordpieceEmbeddingsSentence]

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  7. def calculateSentenceEmbeddings(tokens: Seq[WordpieceTokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int): Seq[Annotation]

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  8. def clone(): AnyRef

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  9. def encode(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]

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    Encode the input sequence to indexes IDs adding padding where necessary

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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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

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

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

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    batch

    batches of sentences

    returns

    batches of vectors for each sentence

  22. val tensorflowWrapper: TensorflowWrapper

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    tensorflowWrapper class

  23. def toString(): String

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  24. final def wait(): Unit

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

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

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