c

com.johnsnowlabs.ml.onnx

OnnxMedicalBertClassification

class OnnxMedicalBertClassification extends MedicalBertClassification

Linear Supertypes
MedicalBertClassification, MedicalClassification, Serializable, Serializable, AnyRef, Any
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Inherited
  1. OnnxMedicalBertClassification
  2. MedicalBertClassification
  3. MedicalClassification
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new OnnxMedicalBertClassification(onnxWrapper: OnnxWrapper, sentenceStartTokenId: Int, sentenceEndTokenId: Int, vocabulary: Map[String, Int], tags: Map[String, Int] = Map(), sentenceSeparator: Option[String] = None)

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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def calculateSoftmax(scores: Array[Float]): Array[Float]
    Definition Classes
    MedicalClassification
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  7. 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
    MedicalClassification
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. def findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]
    Definition Classes
    MedicalBertClassification → MedicalClassification
  12. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  13. def getRawScores(batch: Seq[Array[Int]], useTokenTypes: Boolean = true): Array[Float]
  14. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  15. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  16. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  17. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  18. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  19. val onnxWrapper: OnnxWrapper
  20. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int], useTokenTypes: Boolean = true): Seq[Annotation]
    Definition Classes
    MedicalClassification
  21. def predictSequence(tokenizedSentences: Seq[TokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, coalesceSentences: Boolean = false, tags: Map[String, Int], useTokenTypes: Boolean = true): Seq[Annotation]
    Definition Classes
    MedicalClassification
  22. val sentenceEndTokenId: Int
    Definition Classes
    OnnxMedicalBertClassification → MedicalBertClassification → MedicalClassification
  23. val sentencePadTokenId: Int
    Attributes
    protected
    Definition Classes
    MedicalBertClassification → MedicalClassification
  24. val sentenceStartTokenId: Int
    Definition Classes
    OnnxMedicalBertClassification → MedicalBertClassification → MedicalClassification
  25. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  26. def tag(batch: Seq[Array[Int]], useTokenTypes: Boolean = true): Seq[Array[Array[Float]]]
    Definition Classes
    OnnxMedicalBertClassification → MedicalClassification
  27. def tagSequence(batch: Seq[Array[Int]], useTokenTypes: Boolean = true): Array[Array[Float]]
    Definition Classes
    OnnxMedicalBertClassification → MedicalClassification
  28. def toString(): String
    Definition Classes
    AnyRef → Any
  29. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
    Definition Classes
    MedicalBertClassification → MedicalClassification
  30. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  31. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  33. 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
    MedicalClassification

Inherited from MedicalBertClassification

Inherited from MedicalClassification

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped