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- [讲师] 在笔记本中使用 Python 调试器。
- [Instructor] Using the Python debugger in a notebook.

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在本视频中，我们将学习如何使用 Python 调试器
In this video, we'll learn how to use the Python debugger

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在 Jupyter Notebook 或 Colab 中。
in a Jupyter Notebook or a Colab.

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对于这个例子，我们正在运行代码
For this example, we are running code

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从令牌分类部分，
from the token classification section,

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下载 Conll 数据集，
downloading the Conll dataset,

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稍微看一下数据，
looking a little bit at data,

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在加载分词器之前
before loading a tokenizer

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预处理整个数据集。
to preprocess the whole dataset.

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查看下面链接的课程部分
Check out the section of the course linked below

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了解更多信息。
for more information.

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一旦完成，
Once this is done,

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我们尝试加载训练数据集的八个特征，
we try to load eight features of the training dataset,

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然后将它们批在一起，
and then batch them together,

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使用 tokenizer.pad，
using tokenizer.pad,

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我们得到以下错误。
and we get the following error.

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我们在这里使用 PyTorch，
We use PyTorch here,

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使用 return_tensors=&quot;pt&quot;
with return_tensors="pt"

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但是你会在 TensorFlow 中遇到同样的错误。
but you will get the same error with TensorFlow.

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正如我们在 “如何调试错误？” 中看到的那样。视频，
As we have seen in the "How to debug an error?" video,

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错误消息在回溯的末尾。
the error message is at the end of the traceback.

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在这里，它表明我们应该使用填充，
Here, it indicates us we should use padding,

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我们实际上正在尝试这样做。
which we are actually trying to do.

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所以这根本没有用，
So this is not useful at all,

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我们需要更深入一点
and we will need to go a little deeper

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调试问题。
to debug the problem.

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幸运的是，你可以使用 Python 调试器
Fortunately, you can use the Python debugger

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任何时候你在 Jupyter Notebook 中遇到错误
at any time you get an error in a Jupyter Notebook

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通过在单元格中键入魔法命令 debug。
by typing the magic command, debug, in a cell.

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不要忘记开头的百分比。
Don't forget the percent at the beginning.

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执行该单元格时，
When executing that cell,

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你走到回溯的最底部
you go to the very bottom of the traceback

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你可以在其中键入命令
where you can type commands

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这将帮助你调试脚本。
that will help you debug your script.

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你应该学习的前两个命令，
The first two commands you should learn,

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是 u 和 d，代表向上和向下。
are u and d, for up and down.

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输入 u 和 enter 会让你更上一层楼
Typing u and enter will take you up one step

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在上一条指令的回溯中。
in the traceback to the previous instruction.

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输入 d 然后输入将带你
Typing d and then enter will take you

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在追溯中向下迈出一步。
one step down in the traceback.

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上升两次，我们到达了错误发生的地步。
Going up twice, we get to the point the error was reached.

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为调试器学习的第三个命令是 p，用于打印。
The third command to learn for the debugger is p, for print.

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它允许你打印任何你想要的值。
It allows you to print any value you want.

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例如，键入 p return_tensors 并输入，
For instance, typing p return_tensors and enter,

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我们看到传递给错误函数的值 pt。
we see the value pt that we pass to the bad function.

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我们还可以查看批处理输出
We can also have a look at the batch outputs

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此批处理行编码对象获取。
this batch line coding object gets.

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批量输出字典有点难以挖掘，
The batch outputs dictionary is a bit hard to dig in to,

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因此，让我们深入研究它的较小部分。
so let's dive into smaller pieces of it.

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在调试器内部，你不仅可以打印任何变量
Inside the debugger you can not only print any variable

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还要评估任何表达式，
but also evaluate any expression,

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例如，我们可以查看 input_ids 键
for instance, we can have a look at the input_ids keys

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这个 batch_outputs 对象。
this batch_outputs object.

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或者在这个 batch_outputs 对象的标签键处。
Or at the labels keys of this batch_outputs object.

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这些标签肯定很奇怪：
Those labels are definitely weird:

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它们大小不一，
they are of various sizes,

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我们实际上可以确认，如果我们愿意的话，
which we can actually confirm, if we want,

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通过以最小压缩打印尺寸。
by printing the size with the least compression.

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这是因为 tokenizer 的 pad 方法
This is because the pad method of the tokenizer

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只处理分词器输出：
only takes care of the tokenizer outputs:

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输入 ID、注意掩码和令牌类型 ID，
input IDs, attention mask, and token type IDs,

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所以我们必须自己填充标签
so we have to pad the labels ourselves

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在尝试用它们创建张量之前。
before trying to create a tensor with them.

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一旦你准备好退出 Python 调试器，
Once you are ready to exit the Python debugger,

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你可以按 q 并输入退出。
you can press q and enter for quit.

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修复错误的一种方法
One way to fix the error

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就是手动把 labels 补到最长。
is to manually pad the labels to the longest.

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另一种方法是使用数据整理器
Another way is to use a data collator

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专为令牌分类而设计。
specifically designed for token classification.

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你也可以直接使用 Python 调试器
You can also use a Python debugger directly

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在终端。
in the terminal.

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查看下面的视频链接以了解操作方法。
Check out the video link below to learn how.

