c

com.johnsnowlabs.ml.tensorflow

TensorflowTapas

class TensorflowTapas extends TensorflowBertClassification

Linear Supertypes
TensorflowBertClassification, TensorflowForClassification, Serializable, Serializable, AnyRef, Any
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  1. TensorflowTapas
  2. TensorflowBertClassification
  3. TensorflowForClassification
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Visibility
  1. Public
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Instance Constructors

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

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val _tfBertSignatures: Map[String, String]
    Definition Classes
    TensorflowBertClassification
  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]

    Calculate softmax from returned logits

    Calculate softmax from returned logits

    scores

    logits output from output layer

    Definition Classes
    TensorflowForClassification
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  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. def encodeSequence(seq1: Seq[WordpieceTokenizedSentence], seq2: Seq[WordpieceTokenizedSentence], maxSequenceLength: Int): Seq[Array[Int]]
    Definition Classes
    TensorflowForClassification
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. def findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]
  17. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  19. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  20. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  22. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]
    Definition Classes
    TensorflowForClassification
  24. 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
  25. def predictSpan(documents: Seq[Annotation], maxSentenceLength: Int, caseSensitive: Boolean, mergeTokenStrategy: String = MergeTokenStrategy.vocab): Seq[Annotation]
    Definition Classes
    TensorflowForClassification
  26. def predictTapasSpan(questions: Seq[Annotation], tableAnnotation: Annotation, maxSentenceLength: Int, caseSensitive: Boolean, minCellProbability: Float): Seq[Annotation]
  27. def scoresToLabelForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): String
    Definition Classes
    TensorflowForClassification
  28. val sentenceEndTokenId: Int
  29. val sentencePadTokenId: Int
    Attributes
    protected
    Definition Classes
    TensorflowBertClassificationTensorflowForClassification
  30. val sentenceStartTokenId: Int
  31. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  32. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  33. def tagSequence(batch: Seq[Array[Int]], activation: String): Array[Array[Float]]
  34. def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]])
  35. def tagTapasSpan(batch: Seq[TapasInputData]): (Array[Array[Float]], Array[Int])
  36. val tensorflowWrapper: TensorflowWrapper
  37. def toString(): String
    Definition Classes
    AnyRef → Any
  38. def tokenizeDocument(docs: Seq[Annotation], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
  39. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
  40. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  41. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  42. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  43. 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

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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