Class

com.johnsnowlabs.ml.tensorflow

TensorflowXlnetClassification

Related Doc: package tensorflow

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class TensorflowXlnetClassification extends Serializable with TensorflowForClassification

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TensorflowForClassification, Serializable, Serializable, AnyRef, Any
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  1. TensorflowXlnetClassification
  2. TensorflowForClassification
  3. Serializable
  4. Serializable
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Instance Constructors

  1. new TensorflowXlnetClassification(tensorflowWrapper: TensorflowWrapper, spp: SentencePieceWrapper, configProtoBytes: Option[Array[Byte]] = None, tags: Map[String, Int], signatures: Option[Map[String, String]] = None)

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    tensorflowWrapper

    XLNet Model wrapper with TensorFlow Wrapper

    spp

    XLNet SentencePiece model with SentencePieceWrapper

    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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    AnyRef → Any
  4. val _tfXlnetSignatures: Map[String, String]

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  5. final def asInstanceOf[T0]: T0

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    Any
  6. def calculateSoftmax(scores: Array[Float]): Array[Float]

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    Definition Classes
    TensorflowForClassification
  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def encode(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]

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    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
  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  12. def findIndexedToken(tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tokenPiece: TokenPiece): Option[IndexedToken]

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  13. final def getClass(): Class[_]

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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  16. final def ne(arg0: AnyRef): Boolean

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def predict(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, tags: Map[String, Int]): Seq[Annotation]

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    Definition Classes
    TensorflowForClassification
  20. def predictSequence(tokenizedSentences: Seq[TokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean, coalesceSentences: Boolean = false, tags: Map[String, Int]): Seq[Annotation]

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    Definition Classes
    TensorflowForClassification
  21. val sentenceEndTokenId: Int

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    Attributes
    protected
    Definition Classes
    TensorflowXlnetClassificationTensorflowForClassification
  22. val sentencePadTokenId: Int

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    Attributes
    protected
    Definition Classes
    TensorflowXlnetClassificationTensorflowForClassification
  23. val sentenceStartTokenId: Int

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    Attributes
    protected
    Definition Classes
    TensorflowXlnetClassificationTensorflowForClassification
  24. val spp: SentencePieceWrapper

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    XLNet SentencePiece model with SentencePieceWrapper

  25. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  26. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]

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  27. def tagSequence(batch: Seq[Array[Int]]): Array[Array[Float]]

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  28. val tensorflowWrapper: TensorflowWrapper

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    XLNet Model wrapper with TensorFlow Wrapper

  29. def toString(): String

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    Definition Classes
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  30. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[WordpieceTokenizedSentence]

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  31. final def wait(): Unit

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    @throws( ... )
  32. final def wait(arg0: Long, arg1: Int): Unit

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  33. final def wait(arg0: Long): Unit

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  34. def wordAndSpanLevelAlignmentWithTokenizer(tokenLogits: Array[Array[Float]], tokenizedSentences: Seq[TokenizedSentence], sentence: (WordpieceTokenizedSentence, Int), tags: Map[String, Int]): Seq[Annotation]

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    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

Inherited from Serializable

Inherited from Serializable

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