Use tokenizers from 🤗 Tokenizers
Source: transformers

# Use tokenizers from 🤗 TokenizersThe [`PreTrainedTokenizerFast`] depends on the [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 Tokenizers library can beloaded very simply into 🤗 Transformers.Before getting in the specifics, let's first start by creating a dummy tokenizer in a few lines:```python>>> 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 toa JSON file for future re-use.## Loading directly from the tokenizer objectLet'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:```python>>> 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 tokenizerpage](main_classes/tokenizer) for more information.## Loading from a JSON fileIn order to load a tokenizer from a JSON file, let's first start by saving our tokenizer:```python>>> tokenizer.save("tokenizer.json")```The path to which we saved this file can be passed to the [`PreTrainedTokenizerFast`] initializationmethod using the `tokenizer_file` parameter:```python>>> 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 tokenizerpage](main_classes/tokenizer) for more information.