Video Summary

The paper proposes a new method for training language models that is based on the idea of self-supervised learning. The method is called "SimCLR" and it works by training the model to distinguish between two different versions of the same input. This results in a model that is able to learn the underlying structure of the data without being explicitly told what it is. The paper shows that SimCLR can achieve state-of-the-art results on a variety of natural language processing tasks.