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arXiv · 2407.14039

BERTer: The Efficient One

Pradyumna Saligram·Andrew Lanpouthakoun
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Abstract

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.

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Pradyumna Saligram, Andrew Lanpouthakoun. 2024-07-19. BERTer: The Efficient One. https://arxiv.org/abs/2407.14039

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