How to implement a Bayesian optimizer to fine-tune Transformer hyperparameters

Question

With the help of code can you tell me How to implement a Bayesian optimizer to fine-tune Transformer hyperparameters.

Answer 1

You can fine-tune Transformer hyperparameters by using a Bayesian optimizer like Optuna to efficiently search the hyperparameter space.

Here is the code snippet below:

In the above code we are using the following key points:

• Optuna’s trial object to sample hyperparameters like learning rate, batch size, epochs, and weight decay.

• Hugging Face Trainer API to easily manage training and evaluation.

• IMDB dataset as a sample text classification task.

Hence, this allows efficient and intelligent exploration of hyperparameters to improve model performance with minimal manual tuning.