5 técnicas simples para roberta pires

5 técnicas simples para roberta pires

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

Retrieves sequence ids from a token list that has pelo special tokens added. This method is called when adding

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

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One key difference between RoBERTa and BERT is that RoBERTa was trained on a much larger dataset and using a more effective training procedure. In particular, RoBERTa was trained on a dataset of 160GB of text, which is more than 10 times larger than the dataset used to train BERT.

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

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

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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.

From the BERT’s architecture we remember that during pretraining BERT performs language modeling by trying to predict a certain percentage of masked tokens.

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