BentoML

oh sorry, this is def a bug. Let me put up a PR for this. Thanks for spotting this.

For now what you can do is to save the tokenizer as a custom object
```bentoml.transformers.save_model("model", model, custom_objects={"tokenizer": tokenizer})```
And you can access this tokenizer like so

```bentomodel = bentoml.models.get("model")

tokenizer = bentomodel.custom_objects['tokenizer']```

I notice that you are also running save inside service.py. This is usually discouraged because `service.py` will be forked to child process when running `bentoml serve` , and you probably don’t want to save the model everytime you serve the service.

Usually save should be done after training, but for the case of transformers, you can create a separate file to run before the actual serving time

Oh, Thank you for information. I will use separate file for it.