c

com.johnsnowlabs.ml.tensorflow

TensorflowXlmRoberta

class TensorflowXlmRoberta extends Serializable

Sentence-level embeddings using XLM-RoBERTa. The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale https://arxiv.org/abs/1911.02116 by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.

The abstract from the paper is the following:

This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.

Tips:

- XLM-RoBERTa is a multilingual model trained on 100 different languages. Unlike some XLM multilingual models, it does not require lang parameter to understand which language is used, and should be able to determine the correct language from the input ids. - This implementation is the same as RoBERTa. Refer to the com.johnsnowlabs.nlp.embeddings.RoBertaEmbeddings for usage examples as well as the information relative to the inputs and outputs.

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Instance Constructors

  1. new TensorflowXlmRoberta(tensorflowWrapper: TensorflowWrapper, spp: SentencePieceWrapper, caseSensitive: Boolean = true, configProtoBytes: Option[Array[Byte]] = None, signatures: Option[Map[String, String]] = None)

    tensorflowWrapper

    XlmRoberta Model wrapper with TensorFlowWrapper

    spp

    XlmRoberta SentencePiece model with SentencePieceWrapper

    caseSensitive

    Whether or not the tokenizer should be lowercase

    configProtoBytes

    Configuration for TensorFlow session

    signatures

    Model's inputs and output(s) signatures

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 _tfRoBertaSignatures: Map[String, String]
  5. final def asInstanceOf[T0]: T0
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    Any
  6. def calculateEmbeddings(tokenizedSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int): Seq[WordpieceEmbeddingsSentence]
  7. def calculateSentenceEmbeddings(sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int): Seq[Annotation]
  8. def clone(): AnyRef
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    protected[lang]
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  9. final def eq(arg0: AnyRef): Boolean
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  11. def finalize(): Unit
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    protected[lang]
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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. def prepareBatchInputs(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]
  19. val spp: SentencePieceWrapper
  20. final def synchronized[T0](arg0: ⇒ T0): T0
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  21. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  22. def tagSentence(batch: Seq[Array[Int]]): Array[Array[Float]]
  23. val tensorflowWrapper: TensorflowWrapper
  24. def toString(): String
    Definition Classes
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  25. def tokenizeSentence(sentences: Seq[Sentence], maxSeqLength: Int): Seq[WordpieceTokenizedSentence]
  26. def tokenizeWithAlignment(sentences: Seq[TokenizedSentence], maxSeqLength: Int): Seq[WordpieceTokenizedSentence]
  27. final def wait(): Unit
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  28. final def wait(arg0: Long, arg1: Int): Unit
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  29. final def wait(arg0: Long): Unit
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