Allow for incremental cumulative metric updates in a distributed setup
Currently, using a metric allows for one of the following:
- Per example/batch metrics
- Cumulative metrics over the whole data

What I'd like is to have an efficient way to get cumulative metrics over the examples/batches added so far, in order to display it as part of the progress bar during training/evaluation.

Since most metrics are just an average of per-example metrics (which aren't?), an efficient calculation can be done as follows:
`((score_cumulative * n_cumulative) + (score_new * n_new)) / (n_cumulative+ n_new)`
where `n` and `score`  refer to number of examples and metric score, `cumulative` refers to the cumulative metric and `new` refers to the addition of new examples.

If you don't want to add this capability in the library, a simple solution exists so users can do it themselves:
It is easy to implement for a single process setup, but in a distributed one there is no way to get the correct `n_new`.
The solution for this is to return the number of examples that was used to compute the metrics in `.compute()` by adding the following line here:
https://github.com/huggingface/datasets/blob/5a3221785311d0ce86c2785b765e86bd6997d516/src/datasets/metric.py#L402-L403
```
output["number_of_examples"] = len(predictions)
```
and also remove the log message here so it won't spam:
https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/src/datasets/metric.py#L411

If this change is ok with you, I'll open a pull request.
