Using pipeline('ner'), partial tokens returned when grouped_entities=True
# 🐛 Bug

## Information

Model I am using (Bert, XLNet ...): pipeline('ner', grouped_entities=True)

Language I am using the model on (English, Chinese ...): 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)

## To reproduce

Steps to reproduce the behavior:

```python
from transformers import pipeline
ner = pipeline('ner', grouped_entities=True)
ner("the sapodilla tree is native to Central America")
```

## Expected behavior

The output says that "##di" is one of the named entities.  It doesn't seem like partial tokens should possibly be returned as predicted named entities.  Instead, I imagine that the desired result is that either the entire word "sapodilla" is determined to be an entity group or nothing at all.  Is this a bug or was this quirk consciously chosen to be allowed?

As a side note, another similar quirk here is that something like "U.S." occasionally gives just "U" or "S" as individual named entities, where "U.S." is desired.  I consider this related to the above issue.

## Environment info
- `transformers` version: 3.0.2
- Platform: Linux-5.3.0-1032-azure-x86_64-with-glibc2.10
- Python version: 3.8.1
- PyTorch version (GPU?): 1.5.1 (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: no
- Using distributed or parallel set-up in script?: no
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
