Trainer object empties dataset
## Environment info
- `transformers` version: 4.2.2
- Platform: Linux-4.14.81.bm.20-amd64-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyTorch version (GPU?): 1.7.0 (True)
- Tensorflow version (GPU?): 2.3.1 (True)
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no

## Information

Model I am using (Bert, XLNet ...): XLM-Roberta

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [X] an official GLUE/SQUaD task: NER
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Create a multilingual dataset by concatenating a handful of languages from wikiann
2. Instantiate the model
3. Instantiate the Trainer object

![image](https://user-images.githubusercontent.com/22292468/105478439-79ee0a00-5ca3-11eb-99f3-a648246187af.png)


## Expected behavior
The dataset object should not have been modified by the Trainer object
Your dataset is not really emptied (I agree it looks like this however). It's just viewed without the columns the model can accept. You can restore all your columns with
```
dataset["train"].set_format(columns=list(dataset["train"].features.keys())
``` 
or more simply
```
dataset["train"].reset_format()
```
I agree that this is strange and we're seeing how we can have the same behavior without changing the dataset you pass to Trainer.

Note that you didn't preprocess your data, so you won't be able to train in this example.
<|||||>Hi Sylvain,
Thanks so much for prompt response.

I was not aware of this behaviour so was left very puzzled. I tried what you suggested and indeed got back all my data.

Yes, I agree with you, the data was not yet ready for training. I was just trying to pinpoint at which stage in my script the dataset became "empty".

Thanks again :)