Edit RoBERTa is an extension of BERT with changes to the pretraining procedure. The modifications include: training the model longer, with bigger batches, over more data
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The resulting RoBERTa model appears to be superior to its ancestors on top benchmarks. Despite a more complex configuration, RoBERTa adds only 15M additional parameters maintaining comparable inference speed with BERT.
The authors experimented with removing/adding of NSP loss to different versions and concluded that removing the NSP loss matches or slightly improves downstream task performance
Passing single conterraneo sentences into BERT input hurts the performance, compared to passing sequences consisting of several sentences. One of the most likely hypothesises explaining this phenomenon is the difficulty for a model to learn long-range dependencies only relying on single sentences.
As researchers found, it is slightly better to use dynamic masking meaning that masking is generated uniquely every time a sequence is passed to BERT. Overall, this results in less duplicated data during the training giving an opportunity for a model to work with more various data and masking patterns.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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a dictionary with one or several input Tensors associated to the input names given in the docstring:
This results in 15M and 20M additional parameters for BERT base and BERT large models respectively. The introduced encoding version in RoBERTa demonstrates slightly worse results than before.
Overall, RoBERTa is a powerful and effective language model that has made significant contributions to the field of NLP and has helped to drive progress in a wide range of applications.
dynamically changing the masking pattern applied to the training data. The authors also collect a large new dataset ($text CC-News $) of comparable size to other privately used datasets, to better control for training set size effects
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