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（徽标呼啸而过）
(logo whooshing)

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- pipeline 函数内部发生了什么？
*[译者注: pipeline 作为 流水线 的意思]
- What happens inside the pipeline function?

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在这段视频中，我们将看看实际发生了什么
In this video, we will look at what actually happens

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当我们使用 Transformers 库的
when we use the pipeline function

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pipeline 函数时
of the Transformers library.

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详细来讲，我们将看
More specifically, we will look

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在情绪分析的 pipeline 中，
at the sentiment analysis pipeline,

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它是如何从以下两个句子开始的，
and how it went from the two following sentences,

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将正负标签
to the positive and negative labels

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加上各自的分数。
with their respective scores.

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正如我们在 pipeline 展示中看到的那样，
As we have seen in the pipeline presentation,

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pipeline 分为三个阶段。
there are three stages in the pipeline.

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首先，我们将原始文本转换为数字
First, we convert the raw texts to numbers

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该模型可以通过使用分词器理解。
the model can make sense of using a tokenizer.

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然后这些数字通过模型，
Then those numbers go through the model,

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输出 logits 。
which outputs logits.

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最后，后处理步骤转换
Finally, the post-processing steps transforms

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那些 logits 包含标签和分数。
those logits into labels and scores.

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让我们详细看看这三个步骤
Let's look in detail at those three steps

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以及如何使用 Transformers 库复制它们，
and how to replicate them using the Transformers library,

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从第一阶段开始，分词化。
beginning with the first stage, tokenization.

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分词化过程有几个步骤。
The tokenization process has several steps.

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首先，文本被分成小块, 称之为 token。 
*[译者注: 后面 token-* 均翻译成 分词-*]
First, the text is split into small chunks called tokens.

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它们可以是单词、单词的一部分或标点符号。
They can be words, parts of words or punctuation symbols.

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然后分词器将有一些特殊的 token ，
Then the tokenizer will had some special tokens,

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如果模型期望它们。
if the model expect them.

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这里的模型在开头使用期望 CLS token
Here the model uses expects a CLS token at the beginning

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以及用于分类的句子末尾的 SEP token。
and a SEP token at the end of the sentence to classify.

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最后，分词器将每个 token 与其唯一 ID 匹配
Lastly, the tokenizer matches each token to its unique ID

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在预训练模型的词汇表中。
in the vocabulary of the pretrained model.

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要加载这样的分词器，
To load such a tokenizer,

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Transformers 库提供了 AutoTokenizer API。
the Transformers library provides the AutoTokenizer API.

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这个类最重要的方法是 from_pretrained，
The most important method of this class is from_pretrained,

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这将下载并缓存配置
which will download and cache the configuration

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以及与给定检查点相关联的词汇表。
and the vocabulary associated to a given checkpoint.

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这里默认使用的 checkpoint
Here the checkpoint used by default

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用于情绪分析的 pipeline
for the sentiment analysis pipeline

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是 distilbert-base-uncased-finetuned-sst-2-English。
is distilbert-base-uncased-finetuned-sst-2-English.

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（模糊）
(indistinct)

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我们实例化一个与该检查点关联的分词器，
We instantiate a tokenizer associated with that checkpoint,

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然后给它输入两个句子。
then feed it the two sentences.

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由于这两个句子的大小不同，
Since those two sentences are not of the same size,

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我们需要填充最短的一个
we will need to pad the shortest one

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能够构建一个数组。
to be able to build an array.

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这是由标记器使用选项 padding=True 完成的。
This is done by the tokenizer with the option, padding=True.

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使用 truncation=True，我们确保任何句子
With truncation=True, we ensure that any sentence

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超过模型可以处理的最大值的长度将被截断。
longer than the maximum the model can handle is truncated.

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最后， return_tensors 选项
Lastly, the return_tensors option

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告诉分词器返回一个 PyTorch 张量。
tells the tokenizer to return a PyTorch tensor.

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查看结果，我们看到我们有一本字典
Looking at the result, we see we have a dictionary

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和两个主键
with two keys.

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输入 ID 包含两个句子的 ID，
Input IDs contains the IDs of both sentences,

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应用填充的位置为零。
with zero where the padding is applied.

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第二个键值，注意力掩码，
The second key, attention mask,

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指示已应用填充的位置，
indicates where padding has been applied,

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所以模型不会关注它。
so the model does not pay attention to it.

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这就是分词化步骤中的全部内容。
This is all what is inside the tokenization step.

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现在，让我们来看看第二步，模型。
Now, let's have a look at the second step, the model.

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至于分词器，
As for the tokenizer,

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有一个带有 from_pretrained 方法的 AutoModel API。
there is an AutoModel API with a from_pretrained method.

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它将下载并缓存模型的配置
It will download and cache the configuration of the model

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以及预训练的权重。
as well as the pretrained weights.

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然而，AutoModel API
However, the AutoModel API

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只会实例化模型的主体，
will only instantiate the body of the model,

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那是模型剩下的部分
that is the part of the model that is left

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一旦预训练头被移除。
once the pretraining head is removed.

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它会输出一个高维张量
It will output a high-dimensional tensor

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这是通过的句子的表示，
that is a representation of the sentences passed,

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但这不是直接有用的
but which is not directly useful

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对于我们的分类问题。
for our classification problem.

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这里的张量有两个句子，每个句子有 16 个 token ，
Here the tensor has two sentences, each of 16 tokens,

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最后一个维度是我们模型的隐藏大小，768。
and the last dimension is the hidden size of our model, 768.

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要获得与我们的分类问题相关的输出，
To get an output linked to our classification problem,

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我们需要使用 AutoModelForSequenceClassification 类。
we need to use the AutoModelForSequenceClassification class.

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它与 AutoModel 类完全一样工作，
It works exactly as the AutoModel class,

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除了它会建立一个模型
except that it will build a model

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带分类头。
with a classification head.

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每个常见的 NLP 任务在 Transformers 库中
There is one auto class for each common NLP task

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都有一个自动类
in the Transformers library.

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在给我们的模型两个句子之后，
Here after giving our model the two sentences,

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我们得到一个大小为二乘二的张量，
we get a tensor of size two by two,

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每个句子和每个可能的标签都有一个结果。
one result for each sentence and for each possible label.

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这些输出还不是概率，
Those outputs are not probabilities yet,

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我们可以看到它们的总和不为 1。
we can see they don't sum to 1.

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这是因为 Transformers 库中
This is because each model

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每个模型都会返回 logits 。
of the Transformers library returns logits.

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为了理解这些 logits ，
To make sense of those logits,

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我们需要深入研究管道的第三步也是最后一步。
we need to dig into the third and last step of the pipeline.

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后处理，将 logits 转换为概率，
Post-processing, to convert logits into probabilities,

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我们需要对它们应用 SoftMax 层。
we need to apply a SoftMax layers to them.

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正如我们所见，
As we can see,

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这会将它们转换为正数
this transforms them into positive number

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总结为一个。
that sum up to one.

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最后一步是知道哪些对应
The last step is to know which of those corresponds

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正面或负面的标签。
to the positive or the negative label.

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这是由模型配置的 id2label 字段给出的。
This is given by the id2label field of the model config.

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第一概率，指数零，
The first probabilities, index zero,

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对应负标签，
correspond to the negative label,

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然后第二个，索引一，
and the seconds, index one,

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对应正标签。
correspond to the positive label.

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这就是我们的分类器的构建方式
This is how our classifier built

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使用 pipeline 功能选择了那些标签
with the pipeline function picked those labels

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并计算出这些分数。
and computed those scores.

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既然你知道每个步骤是如何工作的，
Now that you know how each steps works,

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你可以轻松地根据需要调整它们。
you can easily tweak them to your needs.

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（徽标呼啸而过）
(logo whooshing)

