Class

com.johnsnowlabs.ml.tensorflow

TensorflowBert

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class TensorflowBert extends Serializable

BERT (Bidirectional Encoder Representations from Transformers) provides dense vector representations for natural language by using a deep, pre-trained neural network with the Transformer architecture

See https://github.com/JohnSnowLabs/spark-nlp/blob/master/src/test/scala/com/johnsnowlabs/nlp/embeddings/BertEmbeddingsTestSpec.scala for further reference on how to use this API. Sources:

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Instance Constructors

  1. new TensorflowBert(tensorflow: TensorflowWrapper, sentenceStartTokenId: Int, sentenceEndTokenId: Int, configProtoBytes: Option[Array[Byte]] = None)

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    tensorflow

    Bert Model wrapper with TensorFlow Wrapper

    sentenceStartTokenId

    Id of sentence start Token

    sentenceEndTokenId

    Id of sentence end Token.

    configProtoBytes

    Configuration for TensorFlow session Paper: https://arxiv.org/abs/1810.04805 Source: https://github.com/google-research/bert

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. final def asInstanceOf[T0]: T0

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  5. def calculateEmbeddings(sentences: Seq[WordpieceTokenizedSentence], originalTokenSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean): Seq[WordpieceEmbeddingsSentence]

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  6. def calculateSentenceEmbeddings(tokens: Seq[WordpieceTokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int, isLong: Boolean = false): Seq[Annotation]

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  7. def clone(): AnyRef

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  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

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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

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  19. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]

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  20. def tagSentence(batch: Seq[Array[Int]]): Array[Array[Float]]

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  21. def tagSentenceSBert(batch: Seq[Array[Int]]): Array[Array[Float]]

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  22. val tensorflow: TensorflowWrapper

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    Bert Model wrapper with TensorFlow Wrapper

  23. def toString(): String

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  24. final def wait(): Unit

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  25. final def wait(arg0: Long, arg1: Int): Unit

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

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