This repository aggregates academic papers, tutorials, and resources on contrastive learning for NLP. It covers foundational concepts like contrastive loss objectives and sampling strategies, as well as applications including text classification, sentence embeddings, information extraction, machine translation, and generation. The collection serves as a survey of how contrastive learning techniques can improve representation learning in supervised and unsupervised NLP settings.