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com.johnsnowlabs.ml.tensorflow

TensorflowDistilBertClassification

Related Doc: package tensorflow

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class TensorflowDistilBertClassification extends Serializable with TensorflowForClassification

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TensorflowForClassification, Serializable, Serializable, AnyRef, Any
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  1. TensorflowDistilBertClassification
  2. TensorflowForClassification
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Instance Constructors

  1. new TensorflowDistilBertClassification(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])

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    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

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  2. final def ##(): Int

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

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  4. val _tfDistilBertSignatures: Map[String, String]

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  5. final def asInstanceOf[T0]: T0

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  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]
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    @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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    Id of sentence end Token.

    Id of sentence end Token.

    Definition Classes
    TensorflowDistilBertClassificationTensorflowForClassification
  22. val sentencePadTokenId: Int

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    Attributes
    protected
    Definition Classes
    TensorflowDistilBertClassificationTensorflowForClassification
  23. val sentenceStartTokenId: Int

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    Id of sentence start Token

    Id of sentence start Token

    Definition Classes
    TensorflowDistilBertClassificationTensorflowForClassification
  24. final def synchronized[T0](arg0: ⇒ T0): T0

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

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

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  27. val tensorflowWrapper: TensorflowWrapper

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    Bert Model wrapper with TensorFlow Wrapper

  28. def toString(): String

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

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  30. final def wait(): Unit

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

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

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  33. 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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