SimCLR -- An answer to unlabelled data

It is a method of machine learning where the model learns from the supervisory signal of the data unlike supervised learning where separate labels are specified for each observation. It is also known as Representation Learning. Note, the model's learned representation is used for downstream tasks like BERT, where language models are used for text classification tasks. Here, we can use Linear classifiers along with a learned self-supervised model for prediction. Supervised learning requires a large amount of labelled dataset to train a model.