O Melhor Single estratégia a utilizar para roberta pires

O Melhor Single estratégia a utilizar 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

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.

Additionally, RoBERTa uses a dynamic masking technique during training that helps the model learn more robust and generalizable representations of words.

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

As a reminder, the BERT base model was trained on a batch size of 256 sequences for a million steps. The authors tried training BERT Entenda on batch sizes of 2K and 8K and the latter value was chosen for training RoBERTa.

a dictionary with one or several input Tensors associated to the input names given in the docstring:

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, 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:

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

Throughout this article, we will be referring to the official RoBERTa paper which contains in-depth information about the model. In simple words, RoBERTa consists of several independent improvements over the original BERT model — all of the other principles including the architecture stay the same. All of the advancements will be covered and explained in this article.