Loading MPNet from disc: ValueError: An instance of tokenizer class MPNetTokenizer cannot be converted in a Fast tokenizer instance.
## Environment info
- `transformers` version: 4.1.1 (pip version)
- Platform: Ubuntu 20.04
- Python version: 3.7
- PyTorch version (GPU?): Pytorch 1.7 GPU



## Information

Hi,
thanks for adding MPNet. I got quite promising results when using it for generating sentence embeddings.

However, there is an issue when saving and loading the MPNet model (when using version 4.1.1 of transformers, installed via pip):
```python
from transformers import AutoTokenizer, AutoModel

local_dir = 'mpnet-model/'

model = AutoModel.from_pretrained('microsoft/mpnet-base')
tokenizer = AutoTokenizer.from_pretrained('microsoft/mpnet-base')
model.save_pretrained(local_dir)
tokenizer.save_pretrained(local_dir)

#Load tokenizer and model from dir
model = AutoModel.from_pretrained(local_dir)

# The following command will throw an exception
tokenizer = AutoTokenizer.from_pretrained(local_dir)

```

This leads to the following error:
```
File "/home/reimers/miniconda3/envs/sbert/lib/python3.7/site-packages/transformers/convert_slow_tokenizer.py", line 636, in convert_slow_tokenizer
    f"An instance of tokenizer class {tokenizer_class_name} cannot be converted in a Fast tokenizer instance. "
ValueError: An instance of tokenizer class MPNetTokenizer cannot be converted in a Fast tokenizer instance. No converter was found. Currently available slow->fast convertors: ['AlbertTokenizer', 'BartTokenizer', 'BarthezTokenizer', 'BertTokenizer', 'CamembertTokenizer', 'DistilBertTokenizer', 'DPRReaderTokenizer', 'DPRQuestionEncoderTokenizer', 'DPRContextEncoderTokenizer', 'ElectraTokenizer', 'FunnelTokenizer', 'GPT2Tokenizer', 'HerbertTokenizer', 'LayoutLMTokenizer', 'LongformerTokenizer', 'LxmertTokenizer', 'MBartTokenizer', 'MobileBertTokenizer', 'OpenAIGPTTokenizer', 'PegasusTokenizer', 'ReformerTokenizer', 'RetriBertTokenizer', 'RobertaTokenizer', 'SqueezeBertTokenizer', 'T5Tokenizer', 'XLMRobertaTokenizer', 'XLNetTokenizer']
```


Looking into it!

An easy fix for now would be the following:

```python
from transformers import AutoTokenizer, AutoModel

local_dir = 'mpnet-model/'

model = AutoModel.from_pretrained('microsoft/mpnet-base')
tokenizer = AutoTokenizer.from_pretrained('microsoft/mpnet-base')
model.save_pretrained(local_dir)
tokenizer.save_pretrained(local_dir)

#Load tokenizer and model from dir
model = AutoModel.from_pretrained(local_dir)

# The following command will throw an exception
tokenizer = AutoTokenizer.from_pretrained(local_dir, use_fast=False)
```

but that's more of a hack than really solving the underlying problem as it just loads the slow tokenizer. Will check what's going on!
<|||||>The PR linked to the issue adds the required converter so that the above code:

```python
from transformers import AutoTokenizer, AutoModel

local_dir = 'mpnet-model/'

model = AutoModel.from_pretrained('microsoft/mpnet-base')
tokenizer = AutoTokenizer.from_pretrained('microsoft/mpnet-base')
model.save_pretrained(local_dir)
tokenizer.save_pretrained(local_dir)

#Load tokenizer and model from dir
model = AutoModel.from_pretrained(local_dir)

# The following command will throw an exception
tokenizer = AutoTokenizer.from_pretrained(local_dir)
```

should work after merging. It's a bit weird that one can use a FastTokenizer if the model id is `microsoft/mpnet-base` but not if the model is serialized and loaded again...we should maybe think of a better way to prevent such issues in the future. Maybe just not allow one to add a "FastTokenizer" class without adding the corresponding converter? @sgugger @LysandreJik <|||||>Great, thanks for the quick fix.