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- 让我们来看看基于子词的分词。
*[译者注: token, tokenization, tokenizer 等词均译成了 分词*, 实则不翻译最佳]
- Let's take a look at subword based tokenization.

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了解为什么基于子词的分词是
Understanding why subword based tokenization

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是有趣的需要理解
is interesting requires understanding the flaws


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基于单词和基于校正器分词化的缺陷。
of word based and corrector based tokenization.

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如果你还没有看过第一个视频
If you haven't seen the first videos

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关于基于单词和基于字符的标记化
on word based and character based tokenization

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我们建议你观看它们
we recommend you check them out

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在看这个视频之前。
before looking at this video.

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基于子词的分词化介于基于字符
Subword based tokenization lies in between character based

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和基于单词的分词算法之间。
and word based tokenization algorithms.

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这个想法是找到一个中间场
The idea is to find a middle ground

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在很大的词汇表,
between very large vocabularies,

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大量的词汇分词
a large quantity of out vocabulary tokens

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还有在非常相似的词之间意义差
and a loss of meaning across very similar words

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对基于单词的分词器和非常长的序列
for word based tokenizers and very long sequences

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以及意义不大的单个标记
as well as less meaningful individual tokens

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对于基于字符的分词器之间。
for character based tokenizers.

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这些算法依赖于以下原则。
These algorithms rely on the following principle.

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常用词不宜拆分
Frequently used words should not be split

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成更小的子词
into smaller subwords

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而稀有词应该被分解
while rare words should be decomposed

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成有意义的子词。
into meaningful subwords.

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一个例子是 dog 这个词。
An example is the word dog.

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我们想让我们的分词器有一个单一的 ID
We would like to have our tokenizer to have a single ID

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对于 dog 这个词
for the word dog rather

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而不是将其拆分为字母 d o 和 g。
than splitting it into characters d o and g.

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然而，当遇到 dog 这个词时
However, when encountering the word dogs

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我们希望我们的分词从词根上理解这一点
we would like our tokenize to understand that at the root

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这还是 dog 这个词。
this is still the word dog.

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添加 s 后，意思略有改变
With an added "s", that slightly changes the meaning

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同时保持最初的意思。
while keeping the original idea.

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另一个例子是像 tokenization 这样的复杂词
Another example is a complex word like tokenization

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可以拆分成有意义的子词。
which can be split into meaningful subwords.

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这个词的根
The root of the word 

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是 token ，以及 -ization 完整了这个词
is token and -ization completes the root

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以赋予它稍微不同的含义。
to give it a slightly different meaning.

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拆分这个词是有道理的
It makes sense to split the word

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一分为二，token 作为词根，
into two, token as the root of the word,

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标记为单词的开头
labeled as the start of the word

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和 -ization 作为标记的附加信息
and -ization as additional information labeled

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作为单词的完整化。
as a completion of the word.

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如此一来，该模型现在将能够有作用
In turn, the model will now be able to make sense

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在不同情况下的分词。
of token in different situations.

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它会理解这个词的形式: token, tokens, tokenizing
It will understand that the word's token, tokens, tokenizing

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和 tokenization 具有相似的含义并且是相关联的。
and tokenization have a similar meaning and are linked.

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它还将理解 tokenization 、modernization
It's will also understand that tokenization, modernization

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和 immunization ，都有相同的后缀
and immunization, which all have the same suffixes

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可能在相同的句法情况下使用。
are probably used in the same syntactic situations.

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基于子词的分词器通常有办法来
Subword based tokenizers generally have a way to

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识别哪些分词是单词的开头
identify which tokens are a start of word

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以及哪些分词完成了单词的开始。
and which tokens complete start of words.

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所以这里以分词作为单词的开始
So here token as the start of a word 

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以及 ##ization 的开始作为单词的完成。
and ##ization as completion of a word.

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这里 ## 表示 -ization 是单词的一部分
Here, the ## prefix indicates that ization is part of a word 

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而不是它的开始。
rather than the beginning of it.

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 ## 记号
The ## comes

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来自基于单词片算法的 BERT 分词器。
from the BERT tokenizer based on the word piece algorithm.

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其他分词器使用其他前缀, 可以是
Other tokenizes use other prefixes which can be

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用来表示单词的一部分
placed to indicate part of words

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比如在这里或单词的开头。
like in here or start of words instead.

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有很多
There are a lot

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可以使用的不同算法
of different algorithms that can be used

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用于子词分词化
for subword tokenization
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大多数模型都获得了目前最先进的结果
and most models obtaining state-of-the-art results

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今日英语
in English today

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使用一些子词标记化算法。
use some kind of subword tokenization algorithms.

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这些方法有助于减少词汇量
These approaches help in reducing the vocabulary sizes

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通过不同词之间共享的信息
by sharing information across different words

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有能力以理解前缀
having the ability to have prefixes

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和后缀如斯。
and suffixes understood as such.

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他们在非常相似的词中保留意义
They keep meaning across very similar words

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通过识别相似的分词，将它们组合起来。
by recognizing similar tokens, making them up.

