c

com.johnsnowlabs.ml.tensorflow

TensorflowBertClassification

class TensorflowBertClassification extends Serializable with TensorflowForClassification

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TensorflowForClassification, Serializable, Serializable, AnyRef, Any
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  1. TensorflowBertClassification
  2. TensorflowForClassification
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  4. Serializable
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Instance Constructors

  1. new TensorflowBertClassification(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 _tfBertSignatures: Map[String, String]
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def calculateSigmoid(scores: Array[Float]): Array[Float]

    Calcuate sigmoid from returned logits

    Calcuate sigmoid from returned logits

    scores

    logits output from output layer

    Definition Classes
    TensorflowForClassification
  7. def calculateSoftmax(scores: Array[Float]): Array[Float]

    Calculate softmax from returned logits

    Calculate softmax from returned logits

    scores

    logits output from output layer

    Definition Classes
    TensorflowForClassification
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  9. def constructAnnotationForSequenceClassifier(sentence: Sentence, label: String, meta: Array[(String, String)]): Annotation
    Definition Classes
    TensorflowForClassification
  10. def constructMetaForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): Array[(String, String)]
    Definition Classes
    TensorflowForClassification
  11. 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
    TensorflowForClassification
  12. def encodeSequence(seq1: Seq[WordpieceTokenizedSentence], seq2: Seq[WordpieceTokenizedSentence], maxSequenceLength: Int): Seq[Array[Int]]
    Definition Classes
    TensorflowForClassification
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  16. def findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]
  17. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  18. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  19. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  20. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  21. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  22. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]
    Definition Classes
    TensorflowForClassification
  24. 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]
    Definition Classes
    TensorflowForClassification
  25. def predictSpan(documents: Seq[Annotation], maxSentenceLength: Int, caseSensitive: Boolean, mergeTokenStrategy: String = MergeTokenStrategy.vocab): Seq[Annotation]
    Definition Classes
    TensorflowForClassification
  26. def scoresToLabelForSequenceClassifier(tags: Map[String, Int], scores: Array[Float]): String
    Definition Classes
    TensorflowForClassification
  27. val sentenceEndTokenId: Int
  28. val sentencePadTokenId: Int
    Attributes
    protected
    Definition Classes
    TensorflowBertClassificationTensorflowForClassification
  29. val sentenceStartTokenId: Int
  30. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  31. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  32. def tagSequence(batch: Seq[Array[Int]], activation: String): Array[Array[Float]]
  33. def tagSpan(batch: Seq[Array[Int]]): (Array[Array[Float]], Array[Array[Float]])
  34. val tensorflowWrapper: TensorflowWrapper
  35. def toString(): String
    Definition Classes
    AnyRef → Any
  36. def tokenizeDocument(docs: Seq[Annotation], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
  37. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]
  38. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  39. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  40. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  41. 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
    TensorflowForClassification

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Inherited from AnyRef

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