roberta pires No Further um Mistério

roberta pires No Further um Mistério

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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of

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:

Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general

Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

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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model. Initializing with a config file does not load the weights associated with the model, only the configuration.

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

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a dictionary with one or several input Tensors associated to the input names given in the docstring:

The problem arises when we reach the end of a document. In this aspect, researchers compared whether it was worth stopping sampling sentences for such sequences or additionally sampling the first several sentences of the next document (and adding a corresponding separator token between documents). The results showed that the first option is better.

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.

dynamically changing the masking pattern applied to the training data. 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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