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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

model. Initializing with a config file does not load the weights associated with the model, only the configuration.

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This is useful if you want more control over how to convert input_ids indices into associated vectors

As a reminder, the BERT base model was trained on a batch size of 256 sequences for a million steps. The authors tried training BERT on batch sizes of 2K and 8K and the latter value was chosen for training RoBERTa.

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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.

Ultimately, for the final RoBERTa implementation, the authors chose to keep the first two aspects and omit the third one. Despite the observed improvement behind the third insight, researchers did not not proceed with it because otherwise, it would Saiba mais have made the comparison between previous implementations more problematic.

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Throughout this article, we will be referring to the official RoBERTa paper which contains in-depth information about the model. In simple words, RoBERTa consists of several independent improvements over the original BERT model — all of the other principles including the architecture stay the same. All of the advancements will be covered and explained in this article.