extending metric_for_best_model to a list of strings 
# 🚀 Feature request

Hi
Currently ` metric_for_best_model(:obj:`str`, `optional`) ` only covers one metric, for some datasets like MRPC, one have several metrics, like accuracy/F1 or like in STSB both pearson/spearman, and user might need to choose the best based on average on all metrics, it could be helpful to have this option.

This is related to the trainer @sgugger 

thanks. 
This could be added indeed. Note that there is already a workaround: when defining your `compute_metrics` function, you can add a new field with this average:
```
def compute_metrics(eval_preds):
    # Your previous metric computation
    metrics["combined"] = (metrics["accuracy"] + metrics["f1"]) / 2
    return metrics
```
and then you can pass `--metric_for_best_model combined`. This approach is also more flexible as you can completely define the way the combination is done (so you can pick weights for your mean or do something else than the mean).<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.