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（引人注目的介绍）
(intro striking)

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- 在本视频中，我们将研究编码器架构。
- In this video, we'll study the encoder architecture.

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一个流行的仅使用编码器架构的例子是 BURT
An example of a popular encoder only architecture is BURT

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这是同类产品中最受欢迎的模型。
which is the most popular model of its kind.

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让我们首先了解它是如何工作的。
Let's first start by understanding how it works.

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我们将使用一个三个单词的小例子。
We'll use a small example using three words.

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我们使用这些作为输入传递给编码器。
We use these as inputs and pass them through the encoder.

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得到了每个单词的数值表示。
We retrieve a numerical representation of each word.

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例如，在这里，编码器将这三个词，
Here, for example, the encoder converts those three words,

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welcome to NYC，转换为这三个数字序列。
welcome to NYC, in these three sequences of numbers.

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编码器对于每个输入单词
The encoder outputs exactly one sequence of numbers

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精确输出一个数字序列。
per input word.

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这种数值表示也可以称为
This numerical representation can also be called

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特征向量（feature vector）或特征张量（feature tensor）。
a feature vector, or a feature tensor.

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让我们深入研究这种表示。
Let's dive into this representation.

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每个词包含一个向量
It contains one vector per word

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这是通过编码器传递的。
that was passed through the encoder.

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每个向量都是
Each of these vector is a numerical representation

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该词的数字表示。
of the word in question.

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该向量的维度由
The dimension of that vector is defined

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模型的架构所决定。
by the architecture of the model.

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对于基本 BERT 模型，它是 768。
For the base BERT model, it is 768.

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这些表示包含一个词的值，
These representations contain the value of a word,

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但包含上下文化的处理。
but contextualized.

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例如，与单词 "to" 相关联的向量
For example, the vector attributed to the word "to"

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不只是 “to” 这个词的表示。
isn't the representation of only the "to" word.

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它还考虑了它周围的词
It also takes into account the words around it

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我们称之为上下文。
which we call the context.

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正如它在左侧上下文中所看到的那样，
As in it looks to the left context,

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我们正在学习的左边的单词，
the words on the left of the one we're studying,

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这里是 “Welcome” 这个词，
here the word "Welcome",

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和右边的上下文，这里是 “NYC” 这个词，
and the context on the right, here the word "NYC",

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并在给定上下文的情况下输出单词的值。
and it outputs a value for the word given its context.

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因此，它是一个上下文化的值。
It is therefore a contextualized value.

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可以说 768 个值的向量
One could say that the vector of 768 values

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保留文本中单词的含义。
holds the meaning of the word within the text.

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由于自注意力机制，它做到了这一点。
It does this thanks to the self-attention mechanism.

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自注意力机制指的是与单个序列中的不同位置
The self-attention mechanism relates to different positions,

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或不同单词相关联
or different words in a single sequence

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以计算该序列的表示形式。
in order to compute a representation of that sequence.

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正如我们之前所见，这意味着
As we've seen before, this means that

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一个词的结果表示
the resulting representation of a word

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已被序列中的其他词影响。
has been affected by other words in the sequence.

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我们不会在这里深入细节
We won't dive into the specifics here

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我们会提供一些进一步的阅读资料
which will offer some further readings

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如果您想对底层发生了什么
if you want to get a better understanding

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有更好的理解。
at what happens under the hood.

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那么为什么要使用编码器呢？
So why should one use and encoder?

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编码器可用作独立模型
Encoders can be used as stand-alone models

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在各种各样的任务中。
in a wide variety of tasks.

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例如，BERT，
For example, BERT,

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可以说是最著名的 transformer 模型，
arguably the most famous transformer model,

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它是一个独立的编码器模型，
is a standalone encoder model,

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并且在发布时，
and at the time of release,

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它是许多
it'd be the state of the art

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序列分类任务
in many sequence classification tasks,

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问答任务，
question answering tasks,

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和掩码语言建模等任务中的最先进技术。
and mask language modeling to only cite of few.

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编码器非常擅长
The idea is that encoders are very powerful

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提取包含有意义信息的
at extracting vectors that carry

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关于序列的向量。
meaningful information about a sequence.

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这个向量可以被传递给后续的神经元来进一步处理
This vector can then be handled down the road

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以便理解其中包含的信息。
by additional neurons to make sense of them.

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让我们看一些例子
Let's take a look at some examples

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编码器真正闪耀的地方。
where encoder really shine.

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首先，掩码语言建模或 MLM。
First of all, Masked Language Modeling, or MLM.

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这是在一个单词序列中
It's the task of predicting a hidden word

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预测隐藏词的任务。
in a sequence of word.

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在这里，例如，我们在 “My” 和 “is” 之间
Here, for example, we have hidden the word

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隐藏了这个词。
between "My" and "is".

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这是训练 BERT 的目标之一。
This is one of the objectives with which BERT was trained.

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它被训练来预测序列中的隐藏单词。
It was trained to predict hidden words in a sequence.

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编码器在这种情况下尤其大放异彩
Encoders shine in this scenario in particular

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因为双向信息在这里至关重要。
as bi-directional information is crucial here.

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如果我们没有右边的话，
If we didn't have the words on the right,

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“is”、“Sylvain” 和 “.”，
"is", "Sylvain" and the ".",

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那么BERT 将能够识别名称的
then there is very little chance

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作为正确的词
that BERT would have been able to identify name

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的机会就很小。
as the correct word.

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为了预测一个掩码单词
The encoder needs to have a good understanding

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编码器需要对序列有很好的理解
of the sequence in order to predict a masked word

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即使文本在语法上是正确的，
as even if the text is grammatically correct,

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但不一定符合
it does not necessarily make sense

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序列的上下文。
in the context of the sequence.

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如前面提到的，
As mentioned earlier,

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编码器擅长做序列分类。
encoders are good at doing sequence classification.

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情感分析是序列分类的一个例子。
Sentiment analysis is an example of sequence classification.

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该模型的目的是识别序列的情绪。
The model's aim is to identify the sentiment of a sequence.

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它可以从给出的一个序列，
It can range from giving a sequence,

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做出一颗星到五颗星的评级
a rating from one to five stars

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如果进行评论分析
if doing review analysis to giving a positive,

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来对一个序列进行积极或消极的评级
or negative rating to a sequence

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这就是这里显示的内容。
which is what is shown here.

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例如，在这里，给定两个序列，
For example, here, given the two sequences,

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我们使用模型来计算预测，
we use the model to compute a prediction,

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并对序列进行分类
and to classify the sequences

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在这两个类别中，正面和负面。
among these two classes, positive and negative.

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虽然这两个序列非常相似
While the two sequences are very similar

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包含相同的词，
containing the same words,

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意义却完全不同，
the meaning is entirely different,

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并且编码器模型能够掌握这种差异。
and the encoder model is able to grasp that difference.

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（引人注目的结尾）
(outro striking)

