Feature: `push_to_hub` function for metrics.

Add a function to easily push the result of a metric to the hub similar to the Trainer in transformers.

Use-case: a user evaluates a model on a dataset. If this happens with a transformer model and they use the Trainer they can easily do that during training. However, if they are either not using a transformer model, the Trainer or want to do it after training they have to do it manually. Thus it would be nice to easily push the results of a model and framework agnostic model to the hub to add it to a models dataset card.

The workflow would be roughly the following:

metric = load_metric("lvwerra/my_metric")
metric.add(some_predictions, some_references)
metric.compute()
metric.push_to_hub(name="my_new_metric", model="lvwerra/my_model", dataset="lvwerra/my_dataset")

This adds the result of the metric to the meta information of the README.md so it is displayed in the model card.

cc @osanseviero @lhoestq

[lvwerra]

Same, maybe we can add the second option for more “power users” and use it as well under the hood of evaluate.push_to_hub. What do you think?

[osanseviero]

I like the evaluate.push_to_hub(metric, to="lvwerra/my_model") path better.