Roberta Base

RoBERTa is a transformer model pretrained on a large corpus of English data in a self-supervised manner. It uses a masked language modeling (MLM) objective, where it randomly masks 15% of the words in an input sentence and predicts the masked words. The model learns a bidirectional representation of the sentence, which can be used for downstream tasks such as sequence classification, token classification, and question answering. The model was trained on multiple datasets totaling 160GB of text and uses a byte version of Byte-Pair Encoding (BPE) with a vocabulary size of 50,000. It was trained on 1024 V100 GPUs for 500K steps with specific optimizer settings. When fine-tuned on downstream tasks, it has achieved good results on the GLUE benchmark. However, it is important to note that the training data contains biased content from the internet, which can lead to biased predictions.

Use cases

RoBERTa, a pretrained transformer model, has several potential use cases for the technical audience. It can be used for masked language modeling tasks, where it predicts the masked words in a sentence. This model can also be fine-tuned for downstream tasks such as sequence classification, token classification, and question answering. It has been trained on a large corpus of English data, totaling 160GB, and uses a byte version of Byte-Pair Encoding (BPE) with a vocabulary size of 50,000. The model was trained on high-performance GPUs for a large number of steps with specific optimizer settings. When fine-tuned on downstream tasks, RoBERTa has achieved good results on the GLUE benchmark. However, it is important to be aware that the training data includes biased content from the internet, which can result in biased predictions.