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（屏幕呼啸）
(screen whooshing)

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（贴纸弹出）
(sticker popping)

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（屏幕呼啸）
(screen whooshing)

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- 加载自定义数据集。
- Loading a custom dataset.

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尽管 Hugging Face Hub 上承载了
Although the HuggingFace Hub hosts

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超过一千个公共数据集，
over a thousand public datasets,

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你可能仍然需要经常处理存储在你的笔记本电脑
you'll often need to work with data

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或存储在远程服务器上的数据。
that is stored on your laptop or some remote server.

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在本视频中，我们将探讨如何利用 Datasets 库
In this video, we'll explore how the Datasets library

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加载 Hugging Face Hub 以外
can be used to load datasets that aren't available

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的数据集。
on the Hugging Face Hub.

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正如你在此表中所见，Datasets 库
As you can see in this table, the Datasets library

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提供了几个内置脚本
provides several in-built scripts

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以多种格式加载数据集。
to load datasets in several formats.

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要以其中一种格式加载数据集，
To load a dataset in one of these formats,

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你只需要向 load_dataset 函数
you just need to provide the name of the format

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提供格式的名称，
to the load_dataset function,

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并且连同 data_files 参数一起传入
along with a data_files argument

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该参数指向一个或多个文件路径或 URL。
that points to one or more filepaths or URLs.

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要查看实际效果，让我们从加载 CSV 文件开始。
To see this in action, let's start by loading a CSV file.

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在这个例子中，我们首先下载一个数据集
In this example, we first download a dataset

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该数据集是来自 UCI 机器学习库的葡萄酒质量数据。
about wine quality from the UCI machine learning repository.

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由于这是一个 CSV 文件，因此我们指定
Since this is a CSV file, we then specify

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CSV 加载脚本。
the CSV loading script.

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现在，这个脚本需要知道我们的数据在哪里，
Now, this script needs to know where our data is located,

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所以我们提供文件名
so we provide the filename

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作为 data_files 参数的一部分。
as part of the data_files argument.

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并且加载脚本还允许你
And the loading script also allows you

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传递几个关键字参数，
to pass several keyword arguments,

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所以在这里我们也指定了
so here we've also specified

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分号作为分隔符。
that the separator is a semi-colon.

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这样，我们就可以看到数据集
And with that, we can see the dataset

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作为 DatasetDict 对象自动加载，
is loaded automatically as a DatasetDict object,

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CSV 文件中的每一列都代表一个特征。
with each column in the CSV file represented as a feature.

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如果你的数据集位于 GitHub 等远程服务器上
If your dataset is located on some remote server like GitHub

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或其他一些数据仓库，
or some other repository,

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这个过程实际上非常相似。
the process is actually very similar.

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唯一的区别是现在 data_files 参数
The only difference is that now the data_files argument

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指向 URL 而不是本地文件路径。
points to a URL instead of a local filepath.

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现在让我们看一下加载原始文本文件。
Let's now take a look at loading raw text files.

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这种格式在 NLP 中很常见，
This format is quite common in NLP,

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你常常会发现书籍和戏剧
and you'll typically find books and plays

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只是一个包含原始文本的独立文件。
are just a single file with raw text inside.

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在这个例子中，我们有一个莎士比亚戏剧的文本文件
In this example, we have a text file of Shakespeare plays

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存储在 GitHub 仓库中。
that's stored on a GitHub repository.

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正如我们对 CSV 文件所做的那样，
And as we did for CSV files,

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我们只需选择文本加载脚本
we simply choose the text loading script

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并将 data_files 参数指向 URL。
and point the data_files argument to the URL.

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如你所见，这些文件是逐行处理的，
As you can see, these files are processed line-by-line,

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所以原始文本中的空行
so empty lines in the raw text are also represented

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也按照数据集中的一行表示。
as a row in the dataset.

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对于 JSON 文件，有两种主要格式需要了解。
For JSON files, there are two main formats to know about.

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第一个叫做 JSON 行，
The first one is called JSON Lines,

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文件中的每一行都是一个单独的 JSON 对象。
where every row in the file is a separate JSON object.

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对于这些文件，你可以通过选择 JSON 加载脚本
For these files, you can load the dataset

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来加载数据集
by selecting the JSON loading script

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并将 data_files 参数指向文件或 URL。
and pointing the data_files argument to the file or URL.

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在这个例子中，我们加载了一个多行 JSON 的文件
In this example, we've loaded a JSON lines files

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其内容基于 Stack Exchange 问题和答案。
based on Stack Exchange questions and answers.

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另一种格式是嵌套的 JSON 文件。
The other format is nested JSON files.

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这些文件基本上看起来像一本巨大的字典，
These files basically look like one huge dictionary,

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所以 load_dataset 函数允许你指定
so the load_dataset function allow you to specify

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要加载哪个特定关键词。
which specific key to load.

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例如，用于问答的 SQuAD 数据集有它的格式，
For example, the SQuAD dataset for question and answering

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我们可以通过指定我们感兴趣的数据字段
has its format, and we can load it by specifying

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我们对 data 字段感兴趣。
that we're interested in the data field.

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最后要和大家分享的内容是
There is just one last thing to mention

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关于所有这些加载脚本。
about all of these loading scripts.

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你可以有不止一次数据切分，
You can have more than one split,

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你可以通过将数据文件视为字典来加载它们，
you can load them by treating data files as a dictionary,

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并将每个拆分的名称映射到其对应的文件。
and map each split name to its corresponding file.

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其他一切都保持完全不变
Everything else stays completely unchanged

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你可以看到一个例子，
and you can see an example of loading

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加载此 SQuAD 的训练和验证分解步骤都在这里。
both the training and validation splits for this SQuAD here.

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这样，你现在可以加载来自笔记本电脑的数据集，来自 Hugging Face Hub 的数据集，
And with that, you can now load datasets from your laptop,

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或来自任何其他地方的数据集。
the Hugging Face Hub, or anywhere else want.

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（屏幕呼啸）
(screen whooshing)

