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:
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
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The authors also collect a large new dataset ($text CC-News $) of comparable size to other privately used datasets, to better control for training set size effects
model. Initializing with a config file does not load the weights associated with the model, only the configuration.
As researchers found, it is slightly better to use dynamic masking meaning that masking is generated uniquely every time a sequence is passed to BERT. Overall, this results in less duplicated data during the training giving an opportunity for a model to work with more various data and masking patterns.
This is useful if you want more control over how to convert input_ids indices into associated vectors
Simple, colorful and clear - the programming interface from Open Roberta gives children and young people intuitive and playful access to programming. The reason for this is the graphic programming language NEPO® developed at Fraunhofer IAIS:
Recent advancements in NLP showed that increase of the batch size with the appropriate Conheça decrease of the learning rate and the number of training steps usually tends to improve the model’s performance.
A FORMATO masculina Roberto foi introduzida na Inglaterra pelos normandos e passou a ser adotado de modo a substituir o nome inglês antigo Hreodberorth.
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.
From the BERT’s architecture we remember that during pretraining BERT performs language modeling by trying to predict a certain percentage of masked tokens.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.