How to get the best model from optuna hyper parameter search
Hi all,I’ve been playing with optuna for hyperparmeter search. I’m using it with huggingface trainer and wandb.I trained my models with the following codes:best_run = trainer.hyperparameter_search(
    direction="maximize", 
    n_trials=4,
    hp_space=my_hp_space_optuna, 
    compute_objective=my_objective
)What I want to do is to get the best model so that I can make predictions on my test data using it. Thus I’ve tried with the codes below but faced some issues :best_run.predict(encoded_dataset['test'])
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-88-2f2e98dce207> in <module>()
----> 1 best_run.predict(encoded_dataset['test'])

AttributeError: 'BestRun' object has no attribute 'predict'Also, I had a go with the following codes:trainer.predict(encoded_dataset['test'])It did run but it made predictions by using the latest trained model.Any suggestion about this issue? Thanks in advance.training arguments codes:args = TrainingArguments(
    output_dir=output_dir,
    overwrite_output_dir=True,
    save_strategy = "epoch", 
    load_best_model_at_end=True,
    num_train_epochs=4,
    per_device_train_batch_size=64,
    per_device_eval_batch_size=32,
    learning_rate=2e-5, 
    metric_for_best_model= 'eval_pearsonr', 
    evaluation_strategy = "epoch",
    logging_dir=logging_dir,
    logging_strategy="steps",
    logging_steps=1,
    report_to="wandb",
)