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.

This strategy is compared with dynamic masking in which different masking is generated  every time we pass data into the model.

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

Dynamically changing the masking pattern: In BERT architecture, the masking is performed once during data preprocessing, resulting in a single static mask. To avoid using the single static mask, training data is duplicated and masked 10 times, each time with a different mask strategy over 40 epochs thus having 4 epochs with the same mask.

Additionally, RoBERTa uses a dynamic masking technique during training that helps the model learn more robust and generalizable representations of words.

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

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

and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication

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Usando mais do quarenta anos de história a MRV nasceu da vontade do construir imóveis econômicos de modo a criar este sonho dos brasileiros de que querem conquistar 1 novo lar.

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

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