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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:
Instead of using complicated text lines, NEPO uses visual puzzle building blocks that can be easily and intuitively dragged and dropped together in the lab. Even without previous knowledge, initial programming successes can be achieved quickly.
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This is useful if you want more control over how to convert input_ids indices into associated vectors
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
The authors of the paper conducted research for finding an optimal way to model the next sentence prediction task. As a consequence, they found several valuable insights:
It more beneficial to construct input sequences by sampling contiguous sentences from a single document rather than from multiple documents. Normally, sequences are always constructed from contiguous full sentences of a single document so that the Perfeito length is at most 512 tokens.
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.
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 Veja mais gather all the input Tensors