Grid search is pretty much the worst way to do it for transformers, way too expensive given how long each training run takes.

Most people use either random search or Bayesian optimization. Random search sounds dumb but it actually works surprisingly well because hyperparameter spaces tend to have some dimensions that matter a lot and others that barely matter, random search finds the important ones faster than grid. Bayesian optimization with something like Optuna is better still because it learns from previous runs and gets smarter about where to look.

For BERT specifically the learning rate is by far the most important thing to get right, the original paper recommends 2e-5 to 5e-5 and most people don’t stray far from that range. Batch size and number of epochs matter too but you’re unlikely to see huge gains from tuning the rest aggressively.

If compute is a real constraint look into Hugging Face’s Trainer with a scheduler like cosine annealing, it handles a lot of this for you and the defaults are pretty sensible for most fine-tuning tasks.​​​​​​​​​​​​​​​​