Ajudar Os outros perceber as vantagens da imobiliaria camboriu
Ajudar Os outros perceber as vantagens da imobiliaria camboriu
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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:
The problem with the original implementation is the fact that chosen tokens for masking for a given text sequence across different batches are sometimes the same.
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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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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 roberta 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
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a dictionary with one or several input Tensors associated to the input names given in the docstring:
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
Ultimately, for the final RoBERTa implementation, the authors chose to keep the first two aspects and omit the third one. Despite the observed improvement behind the third insight, researchers did not not proceed with it because otherwise, it would have made the comparison between previous implementations more problematic.
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If you choose this second option, there are three possibilities you can use to gather all the input Tensors