If you choose this second option, there are three possibilities you can use to gather all the input Tensors

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.

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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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It can also be used, for example, to test your own programs in advance or to upload playing fields for competitions.

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

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

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.

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

View PDF Abstract:Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices Conheça have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al.