Edit RoBERTa is an extension of BERT with changes to the pretraining procedure. The modifications include: training the model longer, with bigger batches, over more data
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This strategy is compared with dynamic masking in which different masking is generated every time we pass data into the model.
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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
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a dictionary Aprenda mais 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.
Overall, RoBERTa is a powerful and effective language model that has made significant contributions to the field of NLP and has helped to drive progress in a wide range of applications.
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
This is useful if you want more control over how to convert input_ids indices into associated vectors