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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.
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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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
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It more beneficial to construct input sequences by sampling contiguous sentences from a single document rather than from multiple documents. Normally, sequences are always constructed from contiguous full sentences of a single document so that the Completa length is at most 512 tokens.
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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.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
Training with bigger batch sizes & longer sequences: Originally BERT is trained for 1M steps with a batch size of 256 sequences. In this paper, the authors trained the model with 125 steps of 2K sequences and 31K steps with 8k sequences of batch size.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.