The major goal of self-supervised learning is to learn from the lower quality data in NLP and the goal of contrastive learning is to distinguish between similar data and dissimilar data and accordingly map it on the network. Self-supervised contrastive learning is a division of self-supervised learning that focuses on data representation. At the same time, contrastive SSL uses both positive and negative samples from the data.