c

com.johnsnowlabs.ml.tensorflow

MedicalTensorflowDistilBertClassification

class MedicalTensorflowDistilBertClassification extends Serializable with MedicalTensorFLowClassification

Linear Supertypes
MedicalTensorFLowClassification, Serializable, Serializable, AnyRef, Any
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Inherited
  1. MedicalTensorflowDistilBertClassification
  2. MedicalTensorFLowClassification
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
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Visibility
  1. Public
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Instance Constructors

  1. new MedicalTensorflowDistilBertClassification(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])

    tensorflowWrapper

    Bert Model wrapper with TensorFlow Wrapper

    sentenceStartTokenId

    Id of sentence start Token

    sentenceEndTokenId

    Id of sentence end Token.

    configProtoBytes

    Configuration for TensorFlow session

    tags

    labels which model was trained with in order

    signatures

    TF v2 signatures in Spark NLP

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 _tfDistilBertSignatures: Map[String, String]
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def calculateSoftmax(scores: Array[Float]): Array[Float]
    Definition Classes
    MedicalTensorFLowClassification
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. 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
    MedicalTensorFLowClassification
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]
    Definition Classes
    MedicalTensorflowDistilBertClassification → MedicalTensorFLowClassification
  13. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  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. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]
    Definition Classes
    MedicalTensorFLowClassification
  20. def predictSequence(tokenizedSentences: Seq[TokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, coalesceSentences: Boolean = false, tags: Map[String, Int]): Seq[Annotation]
    Definition Classes
    MedicalTensorFLowClassification
  21. val sentenceEndTokenId: Int
    Definition Classes
    MedicalTensorflowDistilBertClassification → MedicalTensorFLowClassification
  22. val sentencePadTokenId: Int
    Attributes
    protected
    Definition Classes
    MedicalTensorflowDistilBertClassification → MedicalTensorFLowClassification
  23. val sentenceStartTokenId: Int
    Definition Classes
    MedicalTensorflowDistilBertClassification → MedicalTensorFLowClassification
  24. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  25. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
    Definition Classes
    MedicalTensorflowDistilBertClassification → MedicalTensorFLowClassification
  26. def tagSequence(batch: Seq[Array[Int]]): Array[Array[Float]]
    Definition Classes
    MedicalTensorflowDistilBertClassification → MedicalTensorFLowClassification
  27. val tensorflowWrapper: TensorflowWrapper
  28. def toString(): String
    Definition Classes
    AnyRef → Any
  29. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
    Definition Classes
    MedicalTensorflowDistilBertClassification → MedicalTensorFLowClassification
  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
    MedicalTensorFLowClassification

Inherited from MedicalTensorFLowClassification

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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