subtitles/zh-CN/42_training-a-new-tokenizer.srt

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（空气呼啸）
(air whooshing)

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- 在这段视频中，我们将一起看到
- In this video we will see together

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训练 tokenizer 的目的是什么，
what is the purpose of training a tokenizer,

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要遵循的关键步骤是什么，
what are the key steps to follow,

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什么是最简单的方法。
and what is the easiest way to do it.

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你会问自己这个问题，
You will ask yourself the question,

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“我应该训练一个新的 tokenizer 吗？”，
"Should I train a new tokenizer?",

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当你计划从头开始训练新模型时。
when you plan to train a new model from scratch.

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一个训练过的分词器会不适合你的语料库
A trained tokenizer would not be suitable for your corpus

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如果你的语料库使用一个不同的语言，
if your corpus is in a different language,

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使用新字符，比如重音符号或大写字母，
uses new characters, such as accents or upper cased letters,

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有特定的词汇，例如医学的或法律的，
has a specific vocabulary, for example medical or legal,

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或使用一个不同的风格，
or uses a different style,

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例如，来自另一个世纪时的语言。
a language from another century for example.

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如果我让 tokenizer 训练在 
If I take the tokenizer trained on

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bert-base-uncased 模型，
the bert-base-uncased model,

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并忽略其归一化步骤，
and ignore its normalization step,

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然后我们就能看到 tokenization 操作
then we can see that the tokenization operation

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在英语句子上，
on the English sentence,

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“这是一个适合我们 tokenizer 的句子”，
"Here is a sentence adapted to our tokenizer",

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产生一个相当令人满意的 token 列表，
produces a rather satisfactory list of tokens,

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从某种意义上说，这八个字的句子
in the sense that this sentence of eight words

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被标记成了九个 token 。
is tokenized into nine tokens.

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另一方面，如果我使用相同的 tokenizer
On the other hand, if I use this same tokenizer

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在孟加拉语的一个句子中，我们看到
on a sentence in Bengali, we see that

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要么一个词被分成许多子 token ，
either a word is divided into many sub tokens,

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或者 tokenizer 不知道任何一个
or that the tokenizer does not know one of

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unicode 字符并仅返回未知 token 。
the unicode characters and returns only an unknown token.

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一个常用词被分成许多 token 的事实
The fact that a common word is split into many tokens

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可能是问题的，因为语言模型
can be problematic, because language models

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只能处理有限长度的 token 序列。
can only handle a sequence of tokens of limited length.

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过度拆分初始文本的 tokenizer
A tokenizer that excessively splits your initial text

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甚至可能影响模型的性能。
may even impact the performance of your model.

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未知的 token 也有问题，
Unknown tokens are also problematic,

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因为模型将无法提取
because the model will not be able to extract

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来自文本未知部分的任何信息。
any information from the unknown part of the text.

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在另一个例子中，我们可以看到
In this other example, we can see that

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tokenizer 替换包含字符的单词
the tokenizer replaces words containing characters

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带有重音和带有未知 token 的大写字母。
with accents and capital letters with unknown tokens.

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最后，如果我们再次使用这个 tokenizer 
Finally, if we use again this tokenizer

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为了标记医学词汇，我们再次看到
to tokenize medical vocabulary, we see again that

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一个单词被分成许多子 token ，
a single word is divided into many sub tokens,

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四个用于 "paracetamol" ，四个用于 "pharyngitis" 。
four for paracetamol, and four for pharyngitis.

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目前的大多数 tokenizer 被 
Most of the tokenizers used by the current

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最先进的语言模型使用的, 需要被训练
state of the art language models need to be trained

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在语料库上, 其与使用的语料库如要相似于
on a corpus that is similar to the one used

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预训练语言模型的。
to pre-train the language model.

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这种训练包括学习规则
This training consists in learning rules

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将文本分成标记。
to divide the text into tokens.

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以及学习和使用这些规则的方法
And the way to learn these rules and use them

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取决于所选的 tokenizer 模型。
depends on the chosen tokenizer model.

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因此，要训练一个新的 tokenizer ，首先需要
Thus, to train a new tokenizer, it is first necessary

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构建由原始文本组成的训练语料库。
to build a training corpus composed of raw texts.

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然后，你必须为你的 tokenizer 选择一种结构。
Then, you have to choose an architecture for your tokenizer.

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这里有两个选项。
Here there are two options.

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最简单的是重用与那个结构, 其相同于
The simplest is to reuse the same architecture as the one

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另一个已经训练过的模型使用的 tokenizer 。
of a tokenizer used by another model already trained.

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否则也可以
Otherwise it is also possible

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彻底设计你的分词器。
to completely design your tokenizer.

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但这需要更多的经验和观察。
But it requires more experience and attention.

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一旦选择了结构，你就可以
Once the architecture is chosen, you can thus

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在你构建的语料库上训练这个 tokenizer 。
train this tokenizer on your constituted corpus.

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最后，你需要做的最后一件事就是保存
Finally, the last thing that you need to do is to save

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能够使用此 tokenizer 的学习规则。
the learned rules to be able to use this tokenizer.

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让我们举个例子。
Let's take an example.

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假设你想在 Python 代码上训练 GPT-2 模型。
Let's say you want to train a GPT-2 model on Python code.

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即使 Python 代码通常是英文的
Even if the Python code is usually in English

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这种类型的文本非常具体，
this type of text is very specific,

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并值得一个训练过的 tokenizer 。
and deserves a tokenizer trained on it.

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为了让你相信这一点，我们将在最后看到
To convince you of this, we will see at the end

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一个例子产生的差异。
the difference produced on an example.

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为此，我们将使用该方法
For that we are going to use the method

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“train_new_from_iterator”，所有快速的 tokenizer
"train_new_from_iterator" that all the fast tokenizers

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在库中有的，因此，
of the library have and thus,

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特别是 GPT2TokenizerFast。
in particular GPT2TokenizerFast.

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这是我们案例中最简单的方法
This is the simplest method in our case

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有一个适合 Python 代码的 tokenizer 。
to have a tokenizer adapted to Python code.

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请记住，第一件事是收集训练语料库。
Remember, the first thing is to gather a training corpus.

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我们将使用 CodeSearchNet 数据集的一个子部分
We will use a subpart of the CodeSearchNet dataset

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仅包含 Python 函数
containing only Python functions

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来自 Github 上的开源库。
from open source libraries on Github.

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这是个好时机。
It's good timing.

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该数据集为数据集库所知
This dataset is known by the datasets library

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我们可以用两行代码加载它。
and we can load it in two lines of code.

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然后，正如 “train_new_from_iterator” 方法所期望的那样
Then, as the "train_new_from_iterator" method expects

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文本列表的迭代器，
a iterator of lists of texts,

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我们创建 “get_training_corpus” 函数，
we create the "get_training_corpus" function,

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这将返回一个迭代器。
which will return an iterator.

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现在我们有了迭代器
Now that we have our iterator

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在我们的 Python 函数语料库中，我们可以加载
on our Python functions corpus, we can load

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GPT-2 分词器结构。
the GPT-2 tokenizer architecture.

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这里 old_tokenizer 不适合我们的语料库。
Here old_tokenizer is not adapted to our corpus.

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但我们只需要
But we only need

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再用一行在我们的新语料库上训练它。
one more line to train it on our new corpus.

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有一点很普遍的是大多数 分词化
An argument that is common to most of the tokenization

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算法, 目前使用的是根据词汇表的大小。
algorithms used at the moment is the size of the vocabulary.

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我们在这里选择值 52,000。
We choose here the value 52,000.

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最后，一旦训练结束，
Finally, once the training is finished,

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我们只需要在本地保存我们的新 tokenizer ，
we just have to save our new tokenizer locally,

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或将其发送到 hub 以便能够重用它
or send it to the hub to be able to reuse it

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之后很容易。
very easily afterwards.

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最后大家一起看个例子有没有用
Finally, let's see together on an example if it was useful

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重新训练一个类似于 GPT-2 的 tokenizer 。
to re-train a tokenizer similar to GPT-2 one.

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使用 GPT-2 的原始 tokenizer 
With the original tokenizer of GPT-2

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我们看到所有的空间都是孤立的，
we see that all spaces are isolated,

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和方法名称 "randn"，
and the method name randn,

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在 Python 代码中相对比较常见，分为两部分。
relatively common in Python code, is split in two.

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使用我们新的分词器，单缩进和双缩进
With our new tokenizer, single and double indentations

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已经学习和方法 "randn"
have been learned and the method randn

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被分词化为一个 token 。
is tokenized into one token.

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有了这个，
And with that,

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你现在知道如何训练你自己的分词器了。
you now know how to train your very own tokenizers now.

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（空气呼啸）
(air whooshing)