
Use tokenizers from 🤗 Tokenizers
The [PreTrainedTokenizerFast] depends on the 🤗 Tokenizers library. The tokenizers obtained from the 🤗 Tokenizers library can be
loaded very simply into 🤗 Transformers.
Before getting in the specifics, let's first start by creating a dummy tokenizer in a few lines:
thon

from tokenizers import Tokenizer
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
from tokenizers.pre_tokenizers import Whitespace
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
tokenizer.pre_tokenizer = Whitespace()
files = []
tokenizer.train(files, trainer)

We now have a tokenizer trained on the files we defined. We can either continue using it in that runtime, or save it to
a JSON file for future re-use.
Loading directly from the tokenizer object
Let's see how to leverage this tokenizer object in the 🤗 Transformers library. The
[PreTrainedTokenizerFast] class allows for easy instantiation, by accepting the instantiated
tokenizer object as an argument:
thon

from transformers import PreTrainedTokenizerFast
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)

This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to the tokenizer
page for more information.
Loading from a JSON file
In order to load a tokenizer from a JSON file, let's first start by saving our tokenizer:
thon

tokenizer.save("tokenizer.json")

The path to which we saved this file can be passed to the [PreTrainedTokenizerFast] initialization
method using the tokenizer_file parameter:
thon

from transformers import PreTrainedTokenizerFast
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")

This object can now be used with all the methods shared by the 🤗 Transformers tokenizers! Head to the tokenizer
page for more information.