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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.
Passing single natural sentences into BERT input hurts the performance, compared to passing sequences consisting of several sentences. One of the most likely hypothesises explaining this phenomenon is the difficulty for a model to learn long-range dependencies only relying on single sentences.
It is also important to keep in mind that batch size increase results in easier parallelization through a special technique called “
The authors of the paper conducted research for finding an optimal way to model the next sentence prediction task. As a consequence, they found several valuable insights:
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A ESTILO masculina Roberto foi introduzida na Inglaterra pelos normandos e passou a ser adotado para substituir o nome inglês antigo Hreodberorth.
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
Com mais de quarenta anos por história a MRV nasceu da vontade de construir imóveis econômicos de modo a fazer este sonho Destes brasileiros de que querem conquistar um novo lar.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.