[i18n-ZH] Translated fast_tokenizers.md to Chinese (huggingface#26910)

docs: translate fast_tokenizers into Chinese

docs/source/zh/_toctree.yml

@@ -9,4 +9,8 @@
 - sections:
   - local: accelerate
     title: 加速分布式训练
-  title: 教程
+  title: 教程
+- sections:
+  - local: fast_tokenizers
+    title: 使用 🤗 Tokenizers 中的分词器
+  title: 开发者指南

docs/source/zh/fast_tokenizers.md

@@ -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
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+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# 使用 🤗 Tokenizers 中的分词器
+
+[`PreTrainedTokenizerFast`] 依赖于 [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) 库。从 🤗 Tokenizers 库获得的分词器可以被轻松地加载到 🤗 Transformers 中。
+
+在了解具体内容之前，让我们先用几行代码创建一个虚拟的分词器：
+
+```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)
+```
+
+现在，我们拥有了一个针对我们定义的文件进行训练的分词器。我们可以在当前运行时中继续使用它，或者将其保存到一个 JSON 文件以供将来重复使用。
+
+## 直接从分词器对象加载
+
+让我们看看如何利用 🤗 Transformers 库中的这个分词器对象。[`PreTrainedTokenizerFast`] 类允许通过接受已实例化的 *tokenizer* 对象作为参数，进行轻松实例化：
+
+```python
+>>> from transformers import PreTrainedTokenizerFast
+
+>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
+```
+
+现在可以使用这个对象，使用 🤗 Transformers 分词器共享的所有方法！前往[分词器页面](main_classes/tokenizer)了解更多信息。
+
+## 从 JSON 文件加载
+
+为了从 JSON 文件中加载分词器，让我们先保存我们的分词器：
+
+```python
+>>> tokenizer.save("tokenizer.json")
+```
+
+我们保存此文件的路径可以通过 `tokenizer_file` 参数传递给 [`PreTrainedTokenizerFast`] 初始化方法：
+
+```python
+>>> from transformers import PreTrainedTokenizerFast
+
+>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
+```
+
+现在可以使用这个对象，使用 🤗 Transformers 分词器共享的所有方法！前往[分词器页面](main_classes/tokenizer)了解更多信息。