c

com.johnsnowlabs.ml.tensorflow

TensorflowXlnet

class TensorflowXlnet extends Serializable

XlnetEmbeddings (XLNet): Generalized Autoregressive Pretraining for Language Understanding

Note that this is a very computationally expensive module compared to word embedding modules that only perform embedding lookups. The use of an accelerator is recommended.

XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking.

XLNet-Large = https://storage.googleapis.com/xlnet/released_models/cased_L-24_H-1024_A-16.zip | 24-layer, 1024-hidden, 16-heads XLNet-Base = https://storage.googleapis.com/xlnet/released_models/cased_L-12_H-768_A-12.zip | 12-layer, 768-hidden, 12-heads. This model is trained on full data (different from the one in the paper).

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Instance Constructors

  1. new TensorflowXlnet(tensorflow: TensorflowWrapper, spp: SentencePieceWrapper, configProtoBytes: Option[Array[Byte]] = None)

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[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean): Seq[WordpieceEmbeddingsSentence]
  6. def clone(): AnyRef
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  9. def finalize(): Unit
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  10. final def getClass(): Class[_]
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  11. def getSpecialTokens(token: String): Array[Int]
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  14. final def ne(arg0: AnyRef): Boolean
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  16. final def notifyAll(): Unit
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  17. val spp: SentencePieceWrapper
  18. final def synchronized[T0](arg0: ⇒ T0): T0
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  19. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  20. val tensorflow: TensorflowWrapper
  21. def toString(): String
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  22. def tokenize(sentences: Seq[TokenizedSentence], maxSeqLength: Int, caseSensitive: Boolean): Seq[Array[WordpieceTokenizedSentence]]
  23. final def wait(): Unit
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