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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.

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

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Language model pretraining has led to significant performance gains but careful comparison between different

Passing single conterraneo sentences into BERT input hurts the performance, compared to passing sequences consisting of several sentences. One of the most likely hypothesises explaining this phenomenon is the difficulty for a model to learn long-range dependencies only relying on single sentences.

As researchers found, it is slightly better to use dynamic masking Descubra 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

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

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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.

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