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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:

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.

Este evento reafirmou o potencial dos mercados regionais brasileiros saiba como impulsionadores do crescimento econômico nacional, e a importância do explorar as oportunidades presentes em cada uma DE regiões.

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

Este Triumph Tower é Ainda mais uma prova de de que a cidade está em constante evoluçãeste e atraindo cada vez mais investidores e moradores interessados em um visual por vida sofisticado e inovador.

Influenciadora A Assessoria da Influenciadora Bell Ponciano informa qual este procedimento para a realização da ação foi aprovada antecipadamente através empresa de que fretou este voo.

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.

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

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

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.