  This paper describes the performance of the team cs60075_team2 at SemEval
2021 Task 1 - Lexical Complexity Prediction. The main contribution of this
paper is to fine-tune transformer-based language models pre-trained on several
text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the
corpora from which the CompLex Dataset was extracted, and others being from
other specific domains such as Finance, Law, etc. We perform ablation studies
on selecting the transformer models and how their individual complexity scores
are aggregated to get the resulting complexity scores. Our method achieves a
best Pearson Correlation of $0.784$ in sub-task 1 (single word) and $0.836$ in
sub-task 2 (multiple word expressions).
