architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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:
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
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Dynamically changing the masking pattern: In BERT architecture, the masking is performed once during data preprocessing, resulting in a single static mask. To avoid using the single static mask, training data is duplicated and masked 10 times, each time with a different mask strategy over quarenta epochs thus having 4 epochs with the same mask.
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model. Initializing with a config file does not load the weights associated with the model, only the configuration.
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 Aprenda mais 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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