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RoBERTa has almost similar architecture as compare to BERT, but in order to improve the results on BERT architecture, the authors made some simple design changes in its architecture and training procedure. These changes are:
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Language model pretraining has led to significant performance gains but careful comparison between different
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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.
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Apart from it, RoBERTa applies all four described aspects above with the same architecture parameters as BERT large. The Completa number of parameters of RoBERTa is 355M.
Recent advancements in NLP showed that increase of the batch size with the appropriate decrease of the learning rate and the number of training steps usually tends to improve the model’s performance.
This is useful if you want more control over how to convert input_ids indices into associated vectors
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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