Detalhes, Ficção e imobiliaria camboriu
Detalhes, Ficção e imobiliaria camboriu
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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.
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.
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The authors experimented with removing/adding of NSP loss to different versions and concluded that removing the NSP loss matches or slightly improves downstream task performance
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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.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors
This is useful if you want more control over how to convert input_ids indices into associated vectors
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
a dictionary with one or several input Tensors associated to the input names given in the docstring:
Attentions weights after the attention softmax, used to Aprenda mais compute the weighted average in the self-attention heads.