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com.johnsnowlabs.ml.tensorflow

TensorflowForClassification

trait TensorflowForClassification extends AnyRef

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Abstract Value Members

  1. abstract def findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]
  2. abstract val sentenceEndTokenId: Int
    Attributes
    protected
  3. abstract val sentencePadTokenId: Int
    Attributes
    protected
  4. abstract val sentenceStartTokenId: Int
    Attributes
    protected
  5. abstract def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  6. abstract def tagSequence(batch: Seq[Array[Int]], activation: String): Array[Array[Float]]
  7. abstract def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]

Concrete 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. final def asInstanceOf[T0]: T0
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  5. def calculateSigmoid(scores: Array[Float]): Array[Float]

    Calcuate sigmoid from returned logits

    Calcuate sigmoid from returned logits

    scores

    logits output from output layer

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

    Calcuate softmax from retruned logits

    Calcuate softmax from retruned logits

    scores

    logits output from output layer

  7. def clone(): AnyRef
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    protected[lang]
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    @throws( ... ) @native()
  8. def constructAnnotationForSequenceClassifier(sentence: Sentence, label: String, meta: Array[(String, String)]): Annotation
  9. def constructMetaForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): Array[(String, String)]
  10. def encode(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]

    Encode the input sequence to indexes IDs adding padding where necessary

  11. final def eq(arg0: AnyRef): Boolean
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  12. def equals(arg0: Any): Boolean
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  13. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]
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  15. def hashCode(): Int
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  16. final def isInstanceOf[T0]: Boolean
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  17. final def ne(arg0: AnyRef): Boolean
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  18. final def notify(): Unit
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  19. final def notifyAll(): Unit
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  20. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]
  21. 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]
  22. def scoresToLabelForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): String
  23. final def synchronized[T0](arg0: ⇒ T0): T0
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  24. def toString(): String
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  25. final def wait(): Unit
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    @throws( ... )
  26. final def wait(arg0: Long, arg1: Int): Unit
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  27. final def wait(arg0: Long): Unit
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  28. 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]

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