Training and evaluation of a RoBERTa model
In general, the training process for GPT-3 involves exposing the model to a massive amount of text data from diverse sources, such as books, articles, websites, and more. By analyzing the patterns, relationships, and language structures within this data, the model learns to predict the likelihood of a word or phrase appearing based on the surrounding context. This learning objective is achieved through a process known as masked language modeling (MLM), where certain words are randomly masked in the input, and the model is tasked with predicting the correct word based on the context.
In this chapter, we train the RoBERTa model, which is a variation of the now-classical BERT model. Instead of using generic sources such as books and Wikipedia articles, we use programs. To make our training task a bit more specific, let us train a model that is capable of “understanding” code from a networking domain – WolfSSL, which is...
