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com.johnsnowlabs.ml.tensorflow

MedicalTensorFLowClassification

trait MedicalTensorFLowClassification extends AnyRef

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MedicalTensorflowBertClassification, MedicalTensorflowDistilBertClassification
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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
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  3. abstract val sentencePadTokenId: Int
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    protected
  4. abstract val sentenceStartTokenId: Int
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    protected
  5. abstract def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  6. abstract def tagSequence(batch: Seq[Array[Int]]): Array[Array[Float]]
  7. abstract def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  5. def calculateSoftmax(scores: Array[Float]): Array[Float]
  6. def clone(): AnyRef
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  7. def encode(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]

    Encode the input sequence to indexes IDs adding padding where necessary

  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: Any): Boolean
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  10. def finalize(): Unit
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  11. final def getClass(): Class[_]
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  14. final def ne(arg0: AnyRef): Boolean
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  16. final def notifyAll(): Unit
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  17. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]
  18. def predictSequence(tokenizedSentences: Seq[TokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, coalesceSentences: Boolean = false, tags: Map[String, Int]): Seq[Annotation]
  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def toString(): String
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  21. final def wait(): Unit
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  22. final def wait(arg0: Long, arg1: Int): Unit
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  23. final def wait(arg0: Long): Unit
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  24. 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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