TokenClassificationPipeline: ignoring subwords
## Environment info

- `transformers` version: 4.4.1
- Platform: Linux-4.15.0-136-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.8.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
Library:
- pipelines: @LysandreJik

## Information

Model I am using (Bert, XLNet ...):
Any NER model, e.g. elastic/distilbert-base-cased-finetuned-conll03-english

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)

Ignoring subwords using the TokenClassificationPipeline. 

## To reproduce

Steps to reproduce the behavior:
```
import transformers
pl = transformers.pipeline('ner', model="elastic/distilbert-base-cased-finetuned-conll03-english", tokenizer="elastic/distilbert-base-cased-finetuned-conll03-english", ignore_labels=[], ignore_subwords=True)
output = pl("Sir Testy McTest is testiful")
```
This outputs:
```
[{'word': 'Sir', 'score': 0.997665524482727, 'entity': 'O', 'index': 1, 'start': 0, 'end': 3}, {'word': 'Test', 'score': 0.7986497282981873, 'entity': 'B-PER', 'index': 2, 'start': 4, 'end': 8}, {'word': '##y', 'score': 0.9581826329231262, 'entity': 'B-PER', 'index': 3, 'start': 8, 'end': 9}, {'word': 'M', 'score': 0.9105736613273621, 'entity': 'I-PER', 'index': 4, 'start': 10, 'end': 11}, {'word': '##c', 'score': 0.9090507626533508, 'entity': 'I-PER', 'index': 5, 'start': 11, 'end': 12}, {'word': '##T', 'score': 0.9545289874076843, 'entity': 'I-PER', 'index': 6, 'start': 12, 'end': 13}, {'word': '##est', 'score': 0.9441993832588196, 'entity': 'I-PER', 'index': 7, 'start': 13, 'end': 16}, {'word': 'is', 'score': 0.9999386072158813, 'entity': 'O', 'index': 8, 'start': 17, 'end': 19}, {'word': 'test', 'score': 0.9998794198036194, 'entity': 'O', 'index': 9, 'start': 20, 'end': 24}, {'word': '##iful', 'score': 0.9999022483825684, 'entity': 'O', 'index': 10, 'start': 24, 'end': 28}]
```

## Expected behavior

The expected behavior would be the subwords token being merged with the preceding token, and their predictions ignored e.g. 
```
{'word': 'Testy', 'score': 0.7986497282981873, 'entity': 'B-PER', 'index': 2, 'start': 4, 'end': 9}
```
instead of 
```
{'word': 'Test', 'score': 0.7986497282981873, 'entity': 'B-PER', 'index': 2, 'start': 4, 'end': 8}, {'word': '##y', 'score': 0.9581826329231262, 'entity': 'B-PER', 'index': 3, 'start': 8, 'end': 9}
```

In the current logic the flag `ignore_subwords` seems to be used only in combination with the `grouped_entities` https://github.com/huggingface/transformers/blob/master/src/transformers/pipelines/token_classification.py#L216 . The output obtained from the example input above, setting both flags as True:
```
[{'entity_group': 'O', 'score': 0.997665524482727, 'word': 'Sir', 'start': 0, 'end': 3}, {'entity_group': 'PER', 'score': 0.8546116948127747, 'word': 'Testy McTest', 'start': 4, 'end': 16}, {'entity_group': 'O', 'score': 0.9999090135097504, 'word': 'is testiful', 'start': 17, 'end': 28}]
```
while setting `grouped_entities=True` and `ignore_subwords=False` outputs
```
[{'entity_group': 'O', 'score': 0.997665524482727, 'word': 'Sir', 'start': 0, 'end': 3}, {'entity_group': 'PER', 'score': 0.7986497282981873, 'word': 'Test', 'start': 4, 'end': 8}, {'entity_group': 'PER', 'score': 0.9353070855140686, 'word': '##y McTest', 'start': 8, 'end': 16}, {'entity_group': 'O', 'score': 0.9999067584673563, 'word': 'is testiful', 'start': 17, 'end': 28}]
```

This seems counterintuitive as the grouped entities shouldn't be fragmented by subwords, and ignoring subwords shouldn't be conditioned on grouping entitities.
Hello! Could you take a look at https://github.com/huggingface/transformers/pull/10568 and let me know if it's interesting for you? It proposes a refactor of the two keywords you mentioned.<|||||>> Hello! Could you take a look at #10568 and let me know if it's interesting for you? It proposes a refactor of the two keywords you mentioned.

Yes! That would solve this issue. Thanks for the pointer. I'll post comments there.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.