c

com.johnsnowlabs.ml.tensorflow

TensorflowRoBerta

class TensorflowRoBerta extends Serializable

TensorFlow backend for RoBERTa and Longformer

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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)

    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]
  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
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  9. def finalize(): Unit
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  10. final def getClass(): Class[_]
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  11. def hashCode(): Int
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  14. final def notify(): Unit
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  15. final def notifyAll(): Unit
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  16. def predict(sentences: Seq[WordpieceTokenizedSentence], originalTokenSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean): Seq[WordpieceEmbeddingsSentence]
  17. def predictSequence(tokens: Seq[WordpieceTokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int): Seq[Annotation]
  18. def prepareBatchInputs(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]

    Encode the input sequence to indexes IDs adding padding where necessary

  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  21. def tagSequence(batch: Seq[Array[Int]]): Array[Array[Float]]

    batch

    batches of sentences

    returns

    batches of vectors for each sentence

  22. val tensorflowWrapper: TensorflowWrapper
  23. def toString(): String
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  24. final def wait(): Unit
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