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- 本节将带来 Hugging Face Datasets 库的快速概览。
- The Hugging Face Datasets library, a quick overview.

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Hugging Face Datasets 库
The Hugging Face Datasets library

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是一个提供 API 来快速下载的库
is a library that provides an API to quickly download

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许多公共数据集并对其进行预处理。
many public datasets and preprocess them.

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在本视频中，我们将探索如何做到这一点。
In this video we will explore how to do that.

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下载部分很简单，
The downloading part is easy,

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使用 load_dataset 函数。
with the load_dataset function.

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你可以直接下载并缓存数据集
You can directly download and cache a dataset

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来自其在数据集中心的标识符。
from its identifier on the Dataset hub.

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在这里，我们从 GLUE 基准中获取 MRPC 数据集，
Here, we fetch the MRPC dataset from the GLUE benchmark,

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这是一个包含成对句子的数据集
which is a dataset containing pairs of sentences

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任务是确定释义。
where the task is to determine the paraphrases.

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load_dataset 函数返回的对象
The object returned by the load_dataset function

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是一个 DatasetDict，它是一种字典
is a DatasetDict, which is a sort of dictionary

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包含我们数据集的每个分割。
containing each split of our dataset.

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我们可以通过使用其名称进行索引来访问每个拆分。
We can access each split by indexing with its name.

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这个拆分然后是 Dataset 类的一个实例，
This split is then an instance of the Dataset class,

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有列，这里是 sentence1，sentence2，
with columns, here sentence1, sentence2,

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标签和 idx，以及行。
label and idx, and rows.

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我们可以通过索引访问给定的元素。
We can access a given element by its index.

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Hugging Face Datasets 库的神奇之处
The amazing thing about the Hugging Face Datasets library

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是所有内容都使用 Apache Arrow 保存到磁盘，
is that everything is saved to disk using Apache Arrow,

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这意味着即使你的数据集很大，
which means that even if your dataset is huge,

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你不会离开 RAM。
you won't get out of RAM.

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只有你请求的元素才会加载到内存中。
Only the elements you request are loaded in memory.

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访问数据集的一部分就像访问一个元素一样简单。
Accessing a slice of your dataset is as easy as one element.

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结果是一个包含值列表的字典
The result is then a dictionary with list of values

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对于每个键。
for each keys.

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这里是标签列表，
Here the list of labels,

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第一句话列表
the list of first sentences

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和第二句话的列表。
and the list of second sentences.

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数据集的特征属性
The features attribute of a Dataset

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为我们提供有关其专栏的更多信息。
gives us more information about its columns.

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特别是，我们可以在这里看到
In particular, we can see here

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它给了我们信件
it gives us the correspondence

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在标签的整数和名称之间。
between the integers and names for the labels.

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零代表不等价，一代表等价。
Zero stands for not equivalent and one for equivalent.

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要预处理数据集的所有元素，
To preprocess all the elements of our dataset,

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我们需要将它们标记化。
we need to tokenize them.

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看看视频 “预处理句子对”
Have a look at the video "Preprocess sentence pairs"

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复习一下，
for a refresher,

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但你只需要发送这两个句子
but you just have to send the two sentences

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带有一些额外的关键字参数的分词器。
to the tokenizer with some additional keyword arguments.

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这里我们表示最大长度为 128
Here we indicate a maximum length of 128

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和垫输入短于这个长度，
and pad inputs shorter than this length,

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截断更长的输入。
truncate inputs that are longer.

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我们把所有这些都放在一个 tokenize_function 中
We put all of this in a tokenize_function

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我们可以直接应用于数据集中的所有拆分
that we can directly apply to all the splits in our dataset

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用地图的方法。
with the map method.

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只要函数返回一个类似字典的对象，
As long as the function returns a dictionary-like object,

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map 方法将根据需要添加新列
the map method will add new columns as needed

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或更新现有的。
or update existing ones.

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加快预处理
To speed up preprocessing

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并利用我们的分词器这一事实
and take advantage of the fact our tokenizer

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由 Rust 支持，
is backed by Rust,

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感谢 Hugging Face Tokenizers 库，
thanks to the Hugging Face Tokenizers library,

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我们可以同时处理多个元素
we can process several elements at the same time

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到我们的 tokenize 函数，使用 batched=True 参数。
to our tokenize function, using the batched=True argument.

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由于分词器可以处理
Since the tokenizer can handle

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第一句话列表，第二句列表，
list of first sentences, list of second sentences,

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tokenize_function 不需要为此更改。
the tokenize_function does not need to change for this.

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你还可以将多处理与 map 方法一起使用。
You can also use multiprocessing with the map method.

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在链接的视频中查看其文档。
Check out its documentation in the linked video.

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完成后，我们几乎可以进行培训了。
Once this is done, we are almost ready for training.

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我们只是删除不再需要的列
We just remove the columns we don't need anymore

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使用 remove_columns 方法，
with the remove_columns method,

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将标签重命名为标签，
rename label to labels,

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因为来自 Hugging Face Transformers 的模型
since the models from the Hugging Face Transformers

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图书馆期望，
library expect that,

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并将输出格式设置为我们想要的后端，
and set the output format to our desired backend,

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火炬、TensorFlow 或 NumPy。
Torch, TensorFlow or NumPy.

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如果需要，我们还可以生成一个简短的数据集样本
If needed, we can also generate a short sample of a dataset

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使用选择方法。
using the select method.

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