  This paper describes our system developed for the SemEval-2024 Task 1:
Semantic Textual Relatedness. The challenge is focused on automatically
detecting the degree of relatedness between pairs of sentences for 14 languages
including both high and low-resource Asian and African languages. Our team
participated in two subtasks consisting of Track A: supervised and Track B:
unsupervised. This paper focuses on a BERT-based contrastive learning and
similarity metric based approach primarily for the supervised track while
exploring autoencoders for the unsupervised track. It also aims on the creation
of a bigram relatedness corpus using negative sampling strategy, thereby
producing refined word embeddings.
