Token classification example only returns labels as -100 for longformer
## 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: 4.0.0
- Platform: Linux-5.4.0-1029-aws-x86_64-with-glibc2.10
- Python version: 3.8.5
- PyTorch version (GPU?): 1.7.0 (True)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?:4 x V100 (16GB)
- Using distributed or parallel set-up in script?: parallel

Also:

tokenizers==0.9.4
datasets==1.1.3

I should note I had the same issue in transformers 3.5.1 too

### Who can help
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 tokenizers: @mfuntowicz
 Longformer/Reformer: @patrickvonplaten

## Information

Model I am using (Bert, XLNet ...): Longformer

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:
* [ ] an official GLUE/SQUaD task: (give the name)
* [X] my own task or dataset: (give details below)

I have made very modifications to the token-classification example ([see here](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py)) to allow me to use my own custom dataset for NER. I have 3 labels O, B-Org, I-ORG.

When processing my inputs with models like `bert-base-cased`, everything runs smoothly, however, when I make the switch to the `allenai/longformer-base-4096` model, the `tokenize_and_align_labels()` that runs via `datasets.map()` only returns labels of -100 for every token.

## To reproduce

Patch file for original `run_ner.py`

```
20d19
< 
44d42
< 
199d196
< 
206,209c203,204
<     text_column_name = "tokens" if "tokens" in column_names else column_names[0]
<     label_column_name = (
<         f"{data_args.task_name}_tags" if f"{data_args.task_name}_tags" in column_names else column_names[1]
<     )
---
>     text_column_name = "words"
>     label_column_name = "ner"
213,217c208,213
<     def get_label_list(labels):
<         unique_labels = set()
<         for label in labels:
<             unique_labels = unique_labels | set(label)
<         label_list = list(unique_labels)
---
>     def get_label_list(label_lists):
>         label_list = list(set(
>             [label
>              for label_list in label_lists
>              for label in label_list]
>         ))
227a224
>         id_to_label = {i: l for i, l in enumerate(label_list)}
239a237,238
>         id2label=id_to_label,
>         label2id=label_to_id,
244a244
>         add_prefix_space=True,
```

minimal `train.json` data
```json
{
    "id": 169,
    "words": [
        "My", "favourite", "thing", "about", "the", "market", "was", "Manteigaria", "which", "sells", "the", "best", "pasteis", "de", "Nata", "in", "the", "city", "in", "my", "opinion", "."
    ],
    "ner": [
        "O", "O", "O", "O", "O", "O", "O", "B-Organisation", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"
    ]
}

```

Example `train-config.json`
```json
{
    "train_file": "./train.json",
    "model_name_or_path": "allenai/longformer-base-4096",
    "output_dir": "./output",
    "max_seq_length": 4096,
    "num_train_epochs": 3,
    "pad_to_max_length": false,
    "per_device_train_batch_size": 1,
    "per_device_eval_batch_size": 1,
    "save_steps": 250,
    "eval_steps": 250,
    "seed": 1,
    "do_train": true,
    "do_eval": false,
    "do_predict": false,
    "fp16": true,
    "evaluation_strategy": "steps",
    "save_total_limit": 1,
}

```

Steps to reproduce the behavior:

1. Apply the patch to the `run_ner.py` file found in the token_classification example on the master branch
2. run `python run_ner.py train-config.json`

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## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
If you print `tokenized_inputs["labels"]` produced by `tokenize_and_align_labels()` you'll see that it is a list of `-100` when using `allenai/longformer-base-4096`. However, if you change this to `bert-base-cased`, it will produce the labels `[[-100, 1, 1, 1, 1, 1, 1, 1, 0, -100, -100, -100, 1, 1, 1, 1, 1, -100, 1, 1, -100, 1, 1, 1, 1, 1, 1, 1, -100]]`, which is correct as `1` is `O` and `0` is `B-Organisation`. (There won't be an `I-Organisation` as this minimal reproducible example doesn't have one).
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