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# 🤗 Tokenizers 라이브러리의 토크나이저 사용하기[[use-tokenizers-from-tokenizers]]
[`PreTrainedTokenizerFast`]는 [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) 라이브러리에 기반합니다. 🤗 Tokenizers 라이브러리의 토크나이저는
🤗 Transformers로 매우 간단하게 불러올 수 있습니다.
구체적인 내용에 들어가기 전에, 몇 줄의 코드로 더미 토크나이저를 만들어 보겠습니다:
```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)
```
우리가 정의한 파일을 통해 이제 학습된 토크나이저를 갖게 되었습니다. 이 런타임에서 계속 사용하거나 JSON 파일로 저장하여 나중에 사용할 수 있습니다.
## 토크나이저 객체로부터 직접 불러오기[[loading-directly-from-the-tokenizer-object]]
🤗 Transformers 라이브러리에서 이 토크나이저 객체를 활용하는 방법을 살펴보겠습니다.
[`PreTrainedTokenizerFast`] 클래스는 인스턴스화된 *토크나이저* 객체를 인수로 받아 쉽게 인스턴스화할 수 있습니다:
```python
>>> from transformers import PreTrainedTokenizerFast
>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
```
이제 `fast_tokenizer` 객체는 🤗 Transformers 토크나이저에서 공유하는 모든 메소드와 함께 사용할 수 있습니다! 자세한 내용은 [토크나이저 페이지](main_classes/tokenizer)를 참조하세요.
## JSON 파일에서 불러오기[[loading-from-a-JSON-file]]
<!--In order to load a tokenizer from a JSON file, let's first start by saving our tokenizer:-->
JSON 파일에서 토크나이저를 불러오기 위해, 먼저 토크나이저를 저장해 보겠습니다:
```python
>>> tokenizer.save("tokenizer.json")
```
JSON 파일을 저장한 경로는 `tokenizer_file` 매개변수를 사용하여 [`PreTrainedTokenizerFast`] 초기화 메소드에 전달할 수 있습니다:
```python
>>> from transformers import PreTrainedTokenizerFast
>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
```
이제 `fast_tokenizer` 객체는 🤗 Transformers 토크나이저에서 공유하는 모든 메소드와 함께 사용할 수 있습니다! 자세한 내용은 [토크나이저 페이지](main_classes/tokenizer)를 참조하세요.