Tuning hyperparameters

Tuning hyperparameters for the T5 model significantly influences its performance on tasks such as web page Q&A, directly affecting how accurately and efficiently the model generates responses. Hyperparameter optimization involves adjusting various parameters that control the model’s training process and architecture to improve its ability to learn and generalize from the training data.

Here’s a list of all the available hyperparameters for the T5 LLM:

• adam_epsilon: This parameter is related to the epsilon value in the Adam optimizer, which prevents division by zero during the optimization process. A typical value is 1e-08.
• cosine_schedule_num_cycles: In a cosine annealing learning rate schedule, this value, set at 0.5, represents the number of cycles during training.
• do_lower_case: A Boolean indicating whether to convert all letters to lowercase during tokenization. For T5, this is typically set to False.
• early_stopping_consider_epochs...