TensorflowDeBertaClassification

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

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2. final def ##(): Int

   

   Definition Classes

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3. final def ==(arg0: Any): Boolean

   

   Definition Classes

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4. val _tfDeBertaSignatures: Map[String, String]

   

5. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

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

   

   Calcuate sigmoid from returned logits

   Calcuate sigmoid from returned logits

   scores

   logits output from output layer

   Definition Classes

   TensorflowForClassification

7. def calculateSoftmax(scores: Array[Float]): Array[Float]

   

   Calcuate softmax from retruned logits

   Calcuate softmax from retruned logits

   scores

   logits output from output layer

   Definition Classes

   TensorflowForClassification

8. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

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   @throws( ... )

9. def constructAnnotationForSequenceClassifier(sentence: Sentence, label: String, meta: Array[(String, String)]): Annotation

   

   Definition Classes

   TensorflowForClassification

10. def constructMetaForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): Array[(String, String)]

    

    Definition Classes

    TensorflowForClassification

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

    

    Encode the input sequence to indexes IDs adding padding where necessary

    Encode the input sequence to indexes IDs adding padding where necessary

    Definition Classes

    TensorflowForClassification

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

    

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13. def equals(arg0: Any): Boolean

    

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14. def finalize(): Unit

    

    Attributes

    protected[java.lang]

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

15. def findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]

    

    Definition Classes

    TensorflowDeBertaClassification → TensorflowForClassification

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

    

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17. def hashCode(): Int

    

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18. final def isInstanceOf[T0]: Boolean

    

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

    

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20. final def notify(): Unit

    

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21. final def notifyAll(): Unit

    

    Definition Classes

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22. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]

    

    Definition Classes

    TensorflowForClassification

23. def predictSequence(tokenizedSentences: Seq[TokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, coalesceSentences: Boolean = false, tags: Map[String, Int], activation: String = ActivationFunction.softmax): Seq[Annotation]

    

    Definition Classes

    TensorflowForClassification

24. def scoresToLabelForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): String

    

    Definition Classes

    TensorflowForClassification

25. val sentenceEndTokenId: Int

    

    Attributes

    protected

    Definition Classes

    TensorflowDeBertaClassification → TensorflowForClassification

26. val sentencePadTokenId: Int

    

    Attributes

    protected

    Definition Classes

    TensorflowDeBertaClassification → TensorflowForClassification

27. val sentenceStartTokenId: Int

    

    Attributes

    protected

    Definition Classes

    TensorflowDeBertaClassification → TensorflowForClassification

28. val spp: SentencePieceWrapper

    

    DeBERTa SentencePiece model with SentencePieceWrapper

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

    

    Definition Classes

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

    

    Definition Classes

    TensorflowDeBertaClassification → TensorflowForClassification

31. def tagSequence(batch: Seq[Array[Int]], activation: String): Array[Array[Float]]

    

    Definition Classes

    TensorflowDeBertaClassification → TensorflowForClassification

32. val tensorflowWrapper: TensorflowWrapper

    

    DeBERTa Model v2 & v3 wrapper with TensorFlow Wrapper

33. def toString(): String

    

    Definition Classes

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

    

    Definition Classes

    TensorflowDeBertaClassification → TensorflowForClassification

35. final def wait(): Unit

    

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

    

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    @throws( ... )

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

    

    Definition Classes

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    @throws( ... )

38. def wordAndSpanLevelAlignmentWithTokenizer(tokenLogits: Array[Array[Float]], tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tags: Map[String, Int]): Seq[Annotation]

    

    Word-level and span-level alignment with Tokenizer https://github.com/google-research/bert#tokenization

    Word-level and span-level alignment with Tokenizer https://github.com/google-research/bert#tokenization

    ### Input orig_tokens = ["John", "Johanson", "'s", "house"] labels = ["NNP", "NNP", "POS", "NN"]

    # bert_tokens == ["[CLS]", "john", "johan", "##son", "'", "s", "house", "[SEP]"] # orig_to_tok_map == [1, 2, 4, 6]

    Definition Classes

    TensorflowForClassification