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results highlight the importance of previously overlooked design choices, and raise questions about the source

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.

O evento reafirmou o potencial Destes mercados regionais brasileiros como impulsionadores do crescimento econômico nacional, e a importância por explorar as oportunidades presentes em cada uma das regiões.

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

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A sua personalidade condiz com alguém satisfeita e Perfeito, de que gosta do olhar a vida através perspectiva1 positiva, enxergando em algum momento este lado positivo por tudo.

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

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

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

The problem arises when we reach the end of a document. In this aspect, researchers compared whether it was worth stopping sampling sentences for such sequences or additionally sampling the first several sentences of the next document (and adding a corresponding separator token between documents). The results showed that the first option is better.

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

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

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