results highlight the importance of previously overlooked design choices, and raise questions about the source
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
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.
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 is also important to keep in mind that batch size increase results in easier parallelization through a special technique called “
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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.
From the BERT’s architecture we remember that during pretraining BERT performs language modeling by trying to predict a certain percentage of masked tokens.
This is useful if you want more control over how to convert input_ids indices into associated vectors