c

com.johnsnowlabs.ml.tensorflow

TensorflowRoBerta

class TensorflowRoBerta extends Serializable

The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach https://arxiv.org/abs/1907.11692> by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018.

It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with much larger mini-batches and learning rates. The abstract from the paper is the following:

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.*

Tips:

- RoBERTa has the same architecture as BERT, but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a different pretraining scheme.

- RoBERTa doesn't have :obj:token_type_ids, you don't need to indicate which token belongs to which segment. Just separate your segments with the separation token :obj:tokenizer.sep_token (or :obj:</s>)

The original code can be found here https://github.com/pytorch/fairseq/tree/master/examples/roberta.

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Instance Constructors

  1. new TensorflowRoBerta(tensorflowWrapper: TensorflowWrapper, sentenceStartTokenId: Int, sentenceEndTokenId: Int, padTokenId: Int, configProtoBytes: Option[Array[Byte]] = None, signatures: Option[Map[String, String]] = None)

    tensorflowWrapper

    tensorflowWrapper class

    sentenceStartTokenId

    special token id for <s>

    sentenceEndTokenId

    special token id for </s>

    configProtoBytes

    ProtoBytes for TensorFlow session config

    signatures

    Model's inputs and output(s) signatures

Value Members

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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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  6. def calculateEmbeddings(sentences: Seq[WordpieceTokenizedSentence], originalTokenSentences: Seq[TokenizedSentence], batchSize: Int, maxSentenceLength: Int, caseSensitive: Boolean): Seq[WordpieceEmbeddingsSentence]
  7. def calculateSentenceEmbeddings(tokens: Seq[WordpieceTokenizedSentence], sentences: Seq[Sentence], batchSize: Int, maxSentenceLength: Int): Seq[Annotation]
  8. def clone(): AnyRef
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  9. def encode(sentences: Seq[(WordpieceTokenizedSentence, Int)], maxSequenceLength: Int): Seq[Array[Int]]

    Encode the input sequence to indexes IDs adding padding where necessary

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  16. final def ne(arg0: AnyRef): Boolean
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  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def tag(batch: Seq[Array[Int]]): Seq[Array[Array[Float]]]
  21. def tagSentence(batch: Seq[Array[Int]]): Array[Array[Float]]

    batch

    batches of sentences

    returns

    batches of vectors for each sentence

  22. val tensorflowWrapper: TensorflowWrapper
  23. def toString(): String
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  24. final def wait(): Unit
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