

TITLE
Two tokenizer initialization methods result in inconsistent segmentation results for special words

COMMENTS
6

REACTIONS
+1: 0
-1: 0
laugh: 0
hooray: 0
heart: 0
rocket: 0
eyes: 0

BODY
### System Info

transformers==4.17.0
torch==1.10.0
python==3.7.3

### Who can help?

@ArthurZucker

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction
```
# xlm-roberta-base directory: git clone https://huggingface.co/xlm-roberta-base
from transformers import XLMRobertaTokenizer
tokenizer_a = XLMRobertaTokenizer.from_pretrained('xlm-roberta-base/')
tokenizer_b = XLMRobertaTokenizer('xlm-roberta-base/sentencepiece.bpe.model')

t = 'texta<s>textb'
print(tokenizer_a.tokenize(t))
print(tokenizer_b.tokenize(t))
```

### Expected behavior

```
# what I expect is that both outputs: 
['▁text', 'a', '<s>', '▁text', 'b']
['▁text', 'a', '<s>', '▁text', 'b']

# However, in reality, their outputs are as follows:
['▁text', 'a', '<s>', '▁text', 'b']
['▁text', 'a', '<', 's', '>', 'text', 'b']
```


Why these two tokenizers have different segmentation results for special words?