Custom tokenizer from Tokenizers library
Hi, thank you for the library.
I have a few questions regarding training from scratch.

I used this as a reference on how to train new language model.
https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb

I wanted to train XLNet for my corpus.

First, I could train with tokenizers by
```
tokenizer = SentencePieceUnigramTokenizer()
tokenizer._tokenizer.normalizer = Sequence([NFKC(), Replace("\n", "")])
tokenizer.train(files=paths, vocab_size=16000, special_tokens=[
    "<s>",
    "</s>",
    "<pad>",
    "<mask>",
    "<unk>",
])
tokenizer.save_model("./new_tokenizer")
```

Then I have to use transformers library to train

```
from transformers import XLNetConfig

config = XLNetConfig(
    vocab_size=16000,
)

from transformers import XLNetTokenizerFast

tokenizer = XLNetTokenizerFast.from_pretrained("./new_tokenizer", max_len=512)

```

this throws is a folder error.  
```
terminate called after throwing an instance of 'std:: iOS failure'
  what():    basic filebuf::underflow error reading the file: Is a directory
Aborted (core dumped)
```

How do I load my trained tokenizer?
https://github.com/huggingface/transformers/issues/11722

found solution here