Why do OpenAI's GPT-2 weights beat mine? Part four: digging into dropout

This blog post explores the author's experiences and findings around fine-tuning transformer models, particularly focusing on the use of dropout. It discusses how models pre-trained with dropout behave differently when fine-tuned with varying dropout settings, and the numerical results from various models are presented to analyze performance. Ultimately, the author concludes that dropout may be more harmful than helpful during fine-tuning, especially for models originally trained without it.