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The original BERT uses a subword-level tokenization with the vocabulary size of 30K which is learned after input preprocessing and using several heuristics. RoBERTa uses bytes instead of unicode characters as the base for subwords and expands the vocabulary size up to 50K without any preprocessing or input tokenization.

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The resulting RoBERTa model appears to be superior to its ancestors on top benchmarks. Despite a more complex configuration, RoBERTa adds only 15M additional parameters maintaining comparable inference speed with BERT.

The authors also collect a large new dataset ($text CC-News $) of comparable size to other privately used datasets, to better control for training set size effects

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One key difference between RoBERTa and BERT is that RoBERTa was trained on a much larger dataset and using a more effective training procedure. In particular, RoBERTa was trained on a dataset of 160GB of text, which is more than 10 times larger than the dataset used to train BERT.

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

Apart from it, RoBERTa applies all four described aspects above with the same architecture parameters as BERT large. The Perfeito number of parameters of RoBERTa is 355M.

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training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of

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

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