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（欢快的音乐）
(upbeat music)

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- [Instructor] 在本视频中，我们将回顾
- [Instructor] In this video, we're going to go over

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HuggingFace Model Hub 导航。
the HuggingFace Model Hub navigation.

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这是 huggingface.co 登陆页面。
This is the huggingface.co landing page.

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要访问 Model Hub，请在右上角
To access the Model Hub, click on the Models tab

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单击 Models 选项卡。
in the upper right corner.

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你会看到这个网页界面，
You should be facing this web interface,

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它被分为几个部分。
which can be split into several parts.

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在左侧，你会找到类别 (category)，
On the left, you'll find categories,

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你可以使用它来自定义模型搜索。
which you can use to tailor your model search.

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第一类是任务 (tasks)。
The first category is the tasks.

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hub 上的模型可用于各种各样的任务。
Models on the hub may be used for a wide variety of tasks.

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这些包括自然语言处理任务，
These include natural language processing tasks,

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比如问答或者文本分类，
such as question answering or text classification,

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但它不仅限于 NLP。
but it isn't only limited to NLP.

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其他领域的其他任务也可用，
Other tasks from other fields are also available,

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例如计算机视觉的图像分类，
such as image classification for computer vision,

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或语音的自动语音识别。
or automatic speech recognition for speech.

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第二类是库 (Library)。
The second category is the libraries.

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hub 上的模型通常共享三个主要框架之一，
Models on the hub usually share one of three backbones,

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PyTorch、TensorFlow 或 JAX。
PyTorch, TensorFlow, or JAX.

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但是，也存在其他主干，例如 rust 或 ONNX。
However, other backbones, such as rust or ONNX also exist.

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最后，这个选项卡也可以
Finally, this tab can also be used

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指定模型来自哪个高级框架。
to specify from which high-level framework the models comes.

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这包括 Transformers ，但也不仅仅是这些。
This includes Transformers, but it isn't limited to it.

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Model Hub 用于托管
The Model Hub is used to host

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许多不同的框架模型，
a lot of different frameworks models,

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我们正在积极寻求
and we're actively looking to host

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支持其他框架模型。
other frameworks models.

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第三类是数据集 (Datasets) 选项卡。
The third category is the datasets tab.

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从此选项卡中选择数据集
Selecting a dataset from this tab means filtering the models

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意味筛选在特定数据集上训练的模型。
so that they were trained on that specific dataset.

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第四类是语言 (Language) 选项卡。
The fourth category is the languages tab.

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从此选项卡中选择一种语言
Selecting a language from this tab

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意味着筛选模型以便它们适配
means filtering the models so that they handle

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所选的语言。
the language selected.

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最后，最后一个类别允许选择模型之间
Finally, the last category allows to choose the license

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共享的许可证 (license)。
with which the model is shared.

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在右侧，你可以在 Model Hub　
On the right, you'll find the models available

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找到可用的模型。
on the Model Hub.

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默认情况下，模型按下载排序。
The models are ordered by downloads by default.

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单击模型时，
When clicking on a model,

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你可以看到它的模型卡。
you should be facing its model card.

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模型卡包含有关模型的信息，
The model card contains information about the model,

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它的描述、预期用途、限制和偏差。
its description, intended use, limitations and biases.

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它还可以展示与如何使用模型相关的代码片段，
It can also show code snippets on how to use the model,

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以及任何相关信息；
as well as any relevant information;

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训练过程，数据处理，
training procedure, data processing,

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评估结果或版权。
evaluation results or copyrights.

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此信息对于要使用的模型至关重要。
This information is crucial for the model to be used.

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制作得越好的模型卡，
The better crafted a model card is,

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越方便其他用户在他们的应用程序中
the easier it will be for other users to leverage your model

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利用你的模型。
in their applications.

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模型卡右侧是 inference API。
On the right of the model card is the inference API.

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可以使用此 interence API
This inference API can be used

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直接玩模型。
to play with the model directly.

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随意修改文字，点击 compute
Feel free to modify the text and click on compute

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看看模型对你的输入有何反应。
to see how would the model behave to your inputs.

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屏幕顶部是模型标签。
At the top of your screen lies the model tags.

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这些包括模型任务，以及任何其他标签
These include the model task, as well as any other tag

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这与我们刚才看到的类别相关。
that is relevant to the categories we have just seen.

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Files and versions 选项卡显示
The Files and versions tab displays the architecture

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模型仓库的架构。
of the repository of that model.

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在这里，我们可以看到定义此模型的所有文件。
Here, we can see all the files that define this model.

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你将看到 Git repository 的所有常用功能：
You'll see all usual features of a Git repository:

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可用的 branch，
the branches available,

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commit 历史
the commit history

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以及 commit diff。
as well as the commit diff.

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在模型卡的顶部
Three different buttons are available

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有三个不同的按钮可用。
at the top of the model card.

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第一个显示如何在代码中
The first one shows how to use

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使用 inference API。
the inference API programmatically.

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第二个展示了如何在 SageMaker 中训练这个模型。
The second one shows how to train this model in SageMaker.

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最后一个显示了如何在适当的库中
And the last one shows how to load that model

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加载该模型。
within the appropriate library.

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对于 BERT，这是 transformers。
For BERT, this is transformers.

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（欢快的音乐）
(upbeat music)

