Hyperparameter Sweep for Selection of Best Pre-trained model
### Feature request

Perhaps I have not been able to figure this out, but the way that the current hyperparameter_search function is set up is that it only allows you to pass in  hyperparameters that are in the TrainingArguments.  However, one key hyperparameter that we are not currently able to pass in  the model types themselves. If I want to pass in a list of models that I would like to try out to hyperparameter space, it is not possible to do with the current set up. For example I want to try out passing in something like this to  my hyperparameter space.

`model_type = trial.suggest_categorical(["bert-base-uncased", "roberta-base", "xlnet"])`

### Motivation

Being able to pass in  model names as part of the hp_space could be very useful especially when one is trying to determine which models might be best for their use case. Compiling a list of different models after reading literature and passing in that compiled list to a  hyperparameter sweep could be very useful. 

It could be used in this  manner: 

```
model_type = trial.suggest_categorical(["bert-base-uncased", "roberta-base", "xlnet"])
epochs = trial.suggest_categorical("epochs", EPOCHS)
    batch_size = trial.suggest_categorical("batch_size", BATCH_SIZE)
    learning_rate = trial.suggest_categorical("learning_rate", LEARNING_RATES)
    scheduler = trial.suggest_categorical("scheduler", SCHEDULERS)
    model_name = trial.suggest_categorical("model_name", MODEL_NAMES)
    
    hp_space = {
        "model_name": model_name,
        "batch_size": batch_size,
        "learning_rate": learning_rate,
        "scheduler": scheduler,
        "epochs": epochs,
    }

## Passing it to  trainer
trainer = Trainer(
            training_args,
            train_dataset=train_dataset,
            eval_dataset=val_dataset,
            data_collator=data_collator,
            tokenizer=tokenizer,
        )
trainer.hyperparameter_search(hp_space=hp_space)
```

### Your contribution

I would love to help work on this issue, if a solution does not possibly exist.
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