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

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:

The problem with the original implementation is the fact that chosen tokens for masking for a given text sequence across different batches are sometimes the same.

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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 40 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.

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

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 have made the comparison between previous implementations more problematic.

A mulher nasceu utilizando todos os requisitos de modo a ser vencedora. Só precisa tomar conhecimento do valor qual representa a coragem de querer.

If you choose this second option, there are three possibilities you can use to gather all the input Tensors