5 técnicas simples para roberta pires

5 técnicas simples para roberta pires

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The original BERT uses a subword-level tokenization with the vocabulary size of 30K which is learned after input preprocessing and using several heuristics. RoBERTa uses bytes instead of unicode characters as the base for subwords and expands the vocabulary size up to 50K without any preprocessing or input tokenization.

The corresponding number of training steps and the learning rate value became respectively 31K and 1e-3.

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

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 quarenta epochs thus having 4 epochs with the same mask.

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

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

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

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

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.

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