Efficiency and Effectiveness in BERT Pretraining: A Comprehensive Analysis

Abstract

This paper presents a comprehensive analysis of efficiency and effectiveness in BERT (Bidirectional Encoder Representations from Transformers) pretraining, a pivotal technique in natural language processing (NLP) that has significantly advanced various NLP tasks. BERT, a transformer-based model, has gained prominence due to its ability to capture contextual information bidirectionally, enabling it to achieve state-of-the-art performance on numerous NLP benchmarks. However, the efficiency and effectiveness of BERT pretraining can vary significantly depending on various factors such as model architecture, dataset size, pretraining objectives, and computational resources. This paper investigates these factors and provides insights into optimizing BERT pretraining for improved efficiency and effectiveness.