[request] make load_metric api intutive
I agree with this proposal. IMO, `num_process` can also be misleading without reading the docs because this option may seem to leverage `multiprocessing` to compute the final result, which is not the case.

@lhoestq @albertvillanova Are you OK with breaking the API for v2.0 and renaming the params as follows:
* `num_process` -> `world_size`
* `process_id` -> `rank` I don't think that's a good idea for 2.0, we may have a new library for metrics anyway.

Note that we will need an API that also makes sense for TF users
```
metric = load_metric('glue', 'mrpc', num_process=num_process, process_id=rank)
```

May I suggest that `num_process` is confusing as it's singular yet expects a plural value and either 
* be deprecated in favor of `num_processes` which is more intuitive since it's plural as its expected value
* or even better why not mimic the established dist environment convention for that purpose, which uses `world_size`. 

Same for `process_id` - why reinvent the naming and needing to explain that this is **NOT** `PID`, when we have `rank` already. That is:

```
metric = load_metric('glue', 'mrpc', world_size=world_size, rank=rank)
```

This then fits like a glove into the pytorch DDP and alike envs. and we just need to call:

* `dist.get_world_size()`
* `dist.get_rank()`

So it'd be as simple as:

```
metric = load_metric('glue', 'mrpc', world_size=dist.get_world_size(), rank=dist.get_rank())
```

From: https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group

* `world_size (int, optional)` – Number of processes participating in the job. Required if store is specified.
* `rank (int, optional)` – Rank of the current process. Required if store is specified.

And may be an example would be useful, so that the user doesn't even need to think about where to get `dist`:
```
import torch.distributed as dist
if dist.is_initialized():
    metric = load_metric(metric_name, world_size=dist.get_world_size(), rank=dist.get_rank())
else:
    metric = load_metric(metric_name)
```

I'm aware this is pytorch-centric, but it's better than no examples, IMHO.

Thank you.