  We explore advanced fine-tuning techniques to boost BERT's performance in
sentiment analysis, paraphrase detection, and semantic textual similarity. Our
approach leverages SMART regularization to combat overfitting, improves
hyperparameter choices, employs a cross-embedding Siamese architecture for
improved sentence embeddings, and introduces innovative early exiting methods.
Our fine-tuning findings currently reveal substantial improvements in model
efficiency and effectiveness when combining multiple fine-tuning architectures,
achieving a state-of-the-art performance score of on the test set, surpassing
current benchmarks and highlighting BERT's adaptability in multifaceted
linguistic tasks.
