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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.
It happens due to the fact that reaching the document boundary and stopping there means that an input sequence will contain less than 512 tokens. For having a similar number of tokens across all batches, the batch size in such cases needs to be augmented. This leads to variable batch size and more complex comparisons which researchers wanted to avoid.
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
This is useful if you want more control over how to convert input_ids indices into associated vectors
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.
It is also important to keep in mind that batch size increase results in easier parallelization through a special technique called “
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Apart from it, RoBERTa applies all four described aspects above with the same architecture parameters as BERT large. The Completa number of parameters of RoBERTa is 355M.
Attentions weights after the attention softmax, used to compute the weighted average in the Informações adicionais self-attention
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RoBERTa is pretrained on a combination of five massive datasets resulting in a total of 160 GB of text data. In comparison, BERT large is pretrained only on 13 GB of data. Finally, the authors increase the number of training steps from 100K to 500K.
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