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zh-CN: small fixes #709

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zh-CN: small fixes #709

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2 changes: 1 addition & 1 deletion chapters/zh-CN/chapter1/1.mdx
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Expand Up @@ -66,7 +66,7 @@
本课程的每一章都设计为在 1 周内完成，每周大约需要 6-8 小时的学习时间。 但是，您可以花尽可能多的时间来完成课程。

- **如果我有问题，我可以在哪里提问？**
如果您对课程的任何部分有疑问，只需单击页面顶部的“*提问*”横幅，系统就会自动重定向到 [Hugging Face 论坛](https:// discuss.huggingface.co/):
如果您对课程的任何部分有疑问，只需单击页面顶部的“*提问*”横幅，系统就会自动重定向到 [Hugging Face 论坛](https://discuss.huggingface.co/):

<img src="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/forum-button.png" alt="Hugging Face论坛链接" width=" 75%">

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2 changes: 1 addition & 1 deletion chapters/zh-CN/chapter1/3.mdx
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Expand Up @@ -79,7 +79,7 @@ classifier(
让我们来看看其中的一些吧！

## 零样本分类 [[零样本分类]]
我们将首先处理一项非常具挑战性的任务，我们需要对尚未标记的文本进行分类。这是实际项目中的常见场景，因为注释文本通常很耗时并且需要领域专业知识。对于这项任务**zero-shot-classification**pipeline非常强大：它允许您直接指定用于分类的标签，因此您不必依赖预训练模型的标签。下面的模型展示了如何使用这两个标签将句子分类为正面或负面——但也可以使用您喜欢的任何其他标签集对文本进行分类。
我们将首先处理一项非常具挑战性的任务，我们需要对尚未标记的文本进行分类。这是实际项目中的常见场景，因为注释文本通常很耗时并且需要领域专业知识。对于这项任务**zero-shot-classification**pipeline非常强大：它允许您直接指定用于分类的标签，因此您不必依赖预训练模型的标签。你已经见过了如何使用那两个标签将句子分类为正面或负面——但也可以使用您喜欢的任何其他标签集对文本进行分类。

```python
from transformers import pipeline
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