🌐 [i18n-KO] Translated fast_tokenizers.mdx to Korean (#22956)

* docs: ko: fast_tokenizer.mdx content - translated Co-Authored-By: Gabriel Yang <gabrielwithhappy@gmail.com> Co-Authored-By: Nayeon Han <nayeon2.han@gmail.com> Co-Authored-By: Hyeonseo Yun <0525_hhgus@naver.com> Co-Authored-By: Sohyun Sim <96299403+sim-so@users.noreply.github.com> Co-Authored-By: Jungnerd <46880056+jungnerd@users.noreply.github.com> Co-Authored-By: Wonhyeong Seo <wonhseo@kakao.com> * Update docs/source/ko/fast_tokenizers.mdx Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com> * Update docs/source/ko/fast_tokenizers.mdx Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com> * Update docs/source/ko/fast_tokenizers.mdx Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com> * Update docs/source/ko/fast_tokenizers.mdx Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com> * Update docs/source/ko/fast_tokenizers.mdx Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com> * Update docs/source/ko/fast_tokenizers.mdx Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com> * Update docs/source/ko/fast_tokenizers.mdx Co-authored-by: Hyeonseo Yun <0525yhs@gmail.com> * Update fast_tokenizers.mdx * Update fast_tokenizers.mdx * Update fast_tokenizers.mdx * Update fast_tokenizers.mdx * Update _toctree.yml --------- Co-authored-by: Gabriel Yang <gabrielwithhappy@gmail.com> Co-authored-by: Nayeon Han <nayeon2.han@gmail.com> Co-authored-by: Hyeonseo Yun <0525_hhgus@naver.com> Co-authored-by: Sohyun Sim <96299403+sim-so@users.noreply.github.com> Co-authored-by: Jungnerd <46880056+jungnerd@users.noreply.github.com> Co-authored-by: Wonhyeong Seo <wonhseo@kakao.com> Co-authored-by: Hyeonseo Yun <0525yhs@gmail.com>
huggingface · May 30, 2023 · a077f71 · a077f71
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diff --git a/docs/source/ko/_toctree.yml b/docs/source/ko/_toctree.yml
@@ -75,8 +75,8 @@
     isExpanded: false
   title: 태스크 가이드
 - sections:
-    - local: in_translation
-      title: (번역중) Use fast tokenizers from 🤗 Tokenizers
+    - local: fast_tokenizers
+      title: 🤗 Tokenizers 라이브러리에서 토크나이저 사용하기
     - local: multilingual
       title: 다국어 모델 추론하기
     - local: in_translation
@@ -673,4 +673,4 @@
     - local: in_translation
       title: (번역중) Utilities for Time Series
     title: (번역중) Internal Helpers
-  title: (번역중) API
+  title: (번역중) API
diff --git a/docs/source/ko/fast_tokenizers.mdx b/docs/source/ko/fast_tokenizers.mdx
@@ -0,0 +1,67 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# 🤗 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.