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
RoBERTa has almost similar architecture as compare to BERT, but in order to improve the results on BERT architecture, the authors made some simple design changes in its architecture and training procedure. These changes are:
The problem with the original implementation is the fact that chosen tokens for masking for a given text sequence across different batches are sometimes the same.
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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.
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model. Initializing with a config file does not load the weights associated with the model, only the configuration.
It can also be used, for example, to test your own programs in advance or to upload playing fields for competitions.
sequence instead of per-token classification). It is the first token of the sequence when built with
Recent advancements in NLP showed that increase of the batch size with the appropriate decrease of the learning rate and the number of training steps usually tends to improve the model’s performance.
This is useful if you want more control over how to convert input_ids indices into associated vectors
, 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code. Subjects:
RoBERTa is pretrained on a combination of five massive datasets resulting in a Completa of 160 GB of text data. Descubra In comparison, BERT large is pretrained only on 13 GB of data. Finally, the authors increase the number of training steps from 100K to 500K.
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