Packages

c

com.johnsnowlabs.ml

MedicalBertClassification

abstract class MedicalBertClassification extends Serializable with MedicalClassification

Linear Supertypes
MedicalClassification, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. MedicalBertClassification
  2. MedicalClassification
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

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

Abstract Value Members

  1. abstract def tag(batch: Seq[Array[Int]], useTokenTypes: Boolean = true): Seq[Array[Array[Float]]]
    Definition Classes
    MedicalClassification
  2. abstract def tagSequence(batch: Seq[Array[Int]], useTokenTypes: Boolean = true): Array[Array[Float]]
    Definition Classes
    MedicalClassification

Concrete 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 extractAssertionLabelAndScore(chunks: Seq[Annotation], tokenLogits: Array[Array[Float]], outputCol: String): Seq[Annotation]
    Attributes
    protected
  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]
  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], useTokenTypes: Boolean = true): Seq[Annotation]
    Definition Classes
    MedicalClassification
  20. def predictAssertion(chunks: Seq[Annotation], sentences: Seq[Annotation], caseSensitive: Boolean, outputCol: String): Seq[Annotation]
  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
  23. val sentencePadTokenId: Int
    Attributes
    protected
    Definition Classes
    MedicalBertClassificationMedicalClassification
  24. val sentenceStartTokenId: Int
  25. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  26. def toString(): String
    Definition Classes
    AnyRef → Any
  27. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
  28. def tokenizeWithAlignmentAssertion(chunks: Seq[Annotation], sentences: Seq[Annotation], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
  29. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  31. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  32. 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 MedicalClassification

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped