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（嘶嘶声）
(whooshing sound)

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- 我们来看一下
- Let's have a look

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在 token 分类管线(pipeline)内。
inside the token classification pipeline.

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在有关管线的视频中，
In the pipeline video,

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我们研究了不同的应用
we looked at the different applications

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transformers 库支持开箱即用的。
the transformers library supports out of the box.

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其中之一是 token 分类。
One of them being token classification.

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例如，预测句子中的每个单词，
For instance, predicting for each word in a sentence,

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他们是否对应于一个人，
whether they correspond to a person,

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一个组织或地点。
an organization, or location.

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我们甚至可以将 token 组合在一起
We can even group together the tokens

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对应同一个实体。
corresponding to the same entity.

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例如，这里构成单词 Sylvain 的所有 token 
For instance, all the tokens that form the word Sylvain here

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或 Hugging 和 Face。
or Hugging and Face.

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因此，token 分类管线 
So, token classification pipeline

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与文本分类管线的工作方式相同
works the same way as a text classification pipeline

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我们在之前的视频中学习过。
we studied in a previous video.

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分为三个步骤。
There are three steps.

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分词化、模型和后处理。
Tokenization, the model, and the post processing.

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前两个步骤相同
The first two steps are identical

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到文本分类管道，
to the text classification pipeline,

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除了我们使用 auto 的 token 分类模型
except we use an auto token classification model

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而不是分类。
instead of a sequence classification one.

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我们分词化我们的文本，然后将其提供给模型。
We tokenize our text, then feed it to the model.

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而不是为每个可能的级别获得一个数字
Instead of getting one number for each possible level

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对于整个句子，
for the whole sentence,

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我们为可能的九个级别中的每一个获得一个数字
we get one number for each of the possible nine levels

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对于句子中的每个 token 。
for every token in the sentence.

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在这里，19。
Here, 19.

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与 transformers 库的所有其他模型一样，
Like all the other models of the transformers library,

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我们的模型输出我们需要转换的 logits
our model outputs logits which we need to turn

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使用 SoftMax 进行预测。
into predictions by using a SoftMax.

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我们还获得了每个 token 的预测标签
We also get the predicted label for each token

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通过最大预测。
by taking the maximum prediction.

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由于 softmax 函数保留顺序，
Since the softmax function preserves the order,

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我们本可以在 logits 上完成
we could have done it on the logits

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如果我们不需要预测。
if we had no need of the predictions.

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模型配置包含标签映射
The model config contains the label mapping

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在其 id2label 字段中。
in its id2label field.

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使用它，我们可以将每个 token 映射到其相应的标签。
Using it, we can map every token to its corresponding label.

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标签 O 对应 “no entity” (没有实体)
The label O corresponds to "no entity"

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这就是为什么我们没有在结果中看到它
which is why we didn't see it in our results

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在第一张幻灯片中。
in the first slide.

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在标签和概率之上，
On top of the label and the probability,

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这些结果包括开始
those results included the start

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和句末字符。
and end character in the sentence.

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我们需要使用偏移映射
We'll need to use the offset mapping

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对于 tokenizer 以得到那些。
of the tokenizer to get those.

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看看下面的视频链接
Look at the video link below

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如果你还不知道它们。
if you don't know about them already.

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然后，遍历每个 token 
Then, looping through each token

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具有不同于 O 的标签，
that has a label distinct from O,

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我们可以建立结果列表
we can build the list of results

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我们得到了我们的第一条管线。
we got with our first pipeline.

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最后一步是将 token 组合在一起
The last step is to group together tokens

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对应于同一个实体。
that corresponds to the same entity.

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这就是为什么我们为每种类型的实体设置了两个标签，
This is why we had two labels for each type of entity,

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例如 I-PER 和 B-PER。
I-PER and B-PER for instance.

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它让我们知道一个 token 是否
It allows us to know if a token

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与前一个实体处于同一实体中。
is in the same entity as a previous one.

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注意有两种方式
Note that there are two ways

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用于 token 分类的标签。
of labeling used for token classification.

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一个，这里是粉红色的，使用 B-PER 标签
One, in pink here, uses the B-PER label

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在每个新实体的开头。
at the beginning of each new entity.

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但另一个蓝色的只使用它
But the other in blue only uses it

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分隔两个相邻的相同类型的实体。
to separate two adjacent entities of the same types.

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在这两种情况下，我们都可以标记一个新实体
In both cases we can flag a new entity

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每次我们看到一个新标签出现时，
each time we see a new label appearing,

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带有 I 或 B 前缀。
either with the I or B prefix.

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然后，将以下所有标记为相同的标记
Then, take all the following tokens labeled the same

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带有 I 标志。
with an I-flag.

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这与偏移映射相结合
This, coupled with the offset mapping

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获取开始和结束字符
to get the start and end characters

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允许我们获得每个实体的文本跨度。
allows us to get the span of texts for each entity.

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（嘶嘶声）
(whooshing sound)

