In `Trainer`, `evaluation_strategy` defaults to `no`, but `save_strategy` defaults to `steps`. Why?
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.9.2
- Platform: Windows-10-10.0.19041-SP0
- Python version: 3.7.4
- PyTorch version (GPU?): 1.9.0+cu111 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no


### Who can help

@sgugger

## Information

I'm using `BERTForMaskedLM`, but it doesn't matter, because it's a general `Trainer` issue.

Perhaps `save_strategy` and `evaluation_strategy` should both have the same default value?

## To reproduce

Steps to reproduce the behavior:

1. In `Trainer`, set `load_best_model_at_end` to `True` without assigning any values to `save_strategy` or `evaluation_strategy`
2. Try to run training

Here's the error trace:
```
ValueError: --load_best_model_at_end requires the save and eval strategy to match, but found
- Evaluation strategy: IntervalStrategy.NO
- Save strategy: IntervalStrategy.STEPS
```

## Expected behavior

Since `load_best_model=True` requires `evaluation_strategy == save_strategy`, it seems reasonable to me that both `evaluation_strategy` and `save_strategy` should be set to `"no"` by default.

If I am wrong and the current behavior is the intended one, I'd like to understand why that's the case. :)

The value of those default is mainly historical, and we can't change it as it would be a breaking change. So even if it does not necessarily make sense when using `--load_best_model_at_end` (which is exactly why that error message was added, to make sure you are not surprised after spending lot of compute in training), we are a bit stuck with it.<|||||>I encountered this issue when my libraries wasn't in the "right" versions,
I found that this issue is gone when the libraries are:
pyorch - 1.4
Adapter transformers - 2.0.0
(note that transformers and adapter-transformers has the same domain name, so make sure that you use adapter-transformers)



Another solution is to add the training parameters
          evaluation_strategy = "no",  
           save_strategy = 'no'
where the values can be - ['no', 'steps', 'epoch']