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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.

Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general

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

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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 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.

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

Attentions weights after the attention softmax, used to compute the Ver mais weighted average in the self-attention

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.

Overall, RoBERTa 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.

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

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