（欢快的音乐） (upbeat music)
- [Instructor] 在本视频中，我们将回顾 - [Instructor] In this video, we're going to go over
HuggingFace Model Hub 导航。 the HuggingFace Model Hub navigation.
这是 huggingface.co 登陆页面。 This is the huggingface.co landing page.
要访问 Model Hub，请在右上角 To access the Model Hub, click on the Models tab
单击 Models 选项卡。 in the upper right corner.
你会看到这个网页界面， You should be facing this web interface,
它被分为几个部分。 which can be split into several parts.
在左侧，你会找到类别 (category)， On the left, you'll find categories,
你可以使用它来自定义模型搜索。 which you can use to tailor your model search.
第一类是任务 (tasks)。 The first category is the tasks.
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.
这些包括自然语言处理任务， These include natural language processing tasks,
比如问答或者文本分类， such as question answering or text classification,
但它不仅限于 NLP。 but it isn't only limited to NLP.
其他领域的其他任务也可用， Other tasks from other fields are also available,
例如计算机视觉的图像分类， such as image classification for computer vision,
或语音的自动语音识别。 or automatic speech recognition for speech.
第二类是库 (Library)。 The second category is the libraries.
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,
PyTorch、TensorFlow 或 JAX。 PyTorch, TensorFlow, or JAX.
但是，也存在其他主干，例如 rust 或 ONNX。 However, other backbones, such as rust or ONNX also exist.
最后，这个选项卡也可以 Finally, this tab can also be used
指定模型来自哪个高级框架。 to specify from which high-level framework the models comes.
这包括 Transformers ，但也不仅仅是这些。 This includes Transformers, but it isn't limited to it.
Model Hub 用于托管 The Model Hub is used to host
许多不同的框架模型， a lot of different frameworks models,
我们正在积极寻求 and we're actively looking to host
支持其他框架模型。 other frameworks models.
第三类是数据集 (Datasets) 选项卡。 The third category is the datasets tab.
从此选项卡中选择数据集 Selecting a dataset from this tab means filtering the models
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.
第四类是语言 (Language) 选项卡。 The fourth category is the languages tab.
从此选项卡中选择一种语言 Selecting a language from this tab
意味着筛选模型以便它们适配 means filtering the models so that they handle
所选的语言。 the language selected.
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
共享的许可证 (license)。 with which the model is shared.
在右侧，你可以在 Model Hub On the right, you'll find the models available
找到可用的模型。 on the Model Hub.
默认情况下，模型按下载排序。 The models are ordered by downloads by default.
单击模型时， When clicking on a model,
你可以看到它的模型卡。 you should be facing its model card.
模型卡包含有关模型的信息， The model card contains information about the model,
它的描述、预期用途、限制和偏差。 its description, intended use, limitations and biases.
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,
以及任何相关信息； as well as any relevant information;
训练过程，数据处理， training procedure, data processing,
评估结果或版权。 evaluation results or copyrights.
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.
制作得越好的模型卡， The better crafted a model card is,
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
利用你的模型。 in their applications.
模型卡右侧是 inference API。 On the right of the model card is the inference API.
可以使用此 interence API This inference API can be used
直接玩模型。 to play with the model directly.
随意修改文字，点击 compute Feel free to modify the text and click on compute
看看模型对你的输入有何反应。 to see how would the model behave to your inputs.
屏幕顶部是模型标签。 At the top of your screen lies the model tags.
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
这与我们刚才看到的类别相关。 that is relevant to the categories we have just seen.
Files and versions 选项卡显示 The Files and versions tab displays the architecture
模型仓库的架构。 of the repository of that model.
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.
你将看到 Git repository 的所有常用功能： You'll see all usual features of a Git repository:
可用的 branch， the branches available,
commit 历史 the commit history
以及 commit diff。 as well as the commit diff.
在模型卡的顶部 Three different buttons are available
有三个不同的按钮可用。 at the top of the model card.
第一个显示如何在代码中 The first one shows how to use
使用 inference API。 the inference API programmatically.
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.
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
加载该模型。 within the appropriate library.
对于 BERT，这是 transformers。 For BERT, this is transformers.
（欢快的音乐） (upbeat music)