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Nevertheless, in the vocabulary size growth in RoBERTa allows to encode almost any word or subword without using the unknown token, compared to BERT. This gives a considerable advantage to RoBERTa as the model can now more fully understand complex texts containing rare words.

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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 Aprenda mais 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.

It can also be used, for example, to test your own programs in advance or to upload playing fields for competitions.

sequence instead of per-token classification). It is the first token of the sequence when built with

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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 have made the comparison between previous implementations more problematic.

a dictionary with one or several input Tensors associated to the input names given in the docstring:

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