> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/transformers/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/transformers/developer-guides/use-fast-tokenizers-from-boinc-ai-tokenizers.md).

# Use fast tokenizers from BOINC AI Tokenizers

The [PreTrainedTokenizerFast](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast) depends on the 🌎[ Tokenizers](https://huggingface.co/docs/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:

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```
>>> 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](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast) class allows for easy instantiation, by accepting the instantiated *tokenizer* object as an argument:

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```
>>> 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](https://huggingface.co/docs/transformers/main_classes/tokenizer) 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:

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```
>>> tokenizer.save("tokenizer.json")
```

The path to which we saved this file can be passed to the [PreTrainedTokenizerFast](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast) initialization method using the `tokenizer_file` parameter:

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```
>>> 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](https://huggingface.co/docs/transformers/main_classes/tokenizer) for more information.