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（徽标嗖嗖声）
(logo whooshes)

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- 如何批量一起输入。
- How to batch inputs together.

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在本视频中，我们将看到
In this video, we'll see

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如何将输入序列一起批量处理。
how to batch input sequences together.

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一般来说，我们要传递的句子
In general, the sentences we want to pass

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进入我们的模型不会都具有相同的长度。
through our model won't all have the same lengths.

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在这里，我们使用模型
Here, we are using the model we saw

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在情绪分析 pipeline 中的
in the sentiment analysis pipeline

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并想对两个句子进行分类。
and want to classify two sentences.

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将它们分词化并映射每个标记时
*[译者注: token, tokenization, tokenizer 等词均译成了 分词*, 实则不翻译最佳]
When tokenizing them and mapping each token

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到其相应的输入 ID，
to its corresponding input IDs,

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我们得到两个不同长度的列表。
we get two lists of different lengths.

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尝试创建 tensor 和 NumPy 数组
Trying to create a tensor and NumPy array

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从这两个列表中将导致错误
from those two lists will result in an error

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因为所有数组和张量都应该是矩形的。
because all arrays and tensors should be rectangular.

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克服此困难的一种方法
One way to overcome this limit

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就是让第二句和第一句一样长
is to make the second sentence the same length as the first

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通过根据需要多次添加特殊分词。
by adding a special token as many times as necessary.

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另一种方法是截断第一个序列
Another way would be to truncate the first sequence

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到第二个的长度，但我们会失去很多
to the length of the second, but we would then lose a lot

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可能需要的信息
of information that may be necessary

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来正确地对句子进行分类。
to properly classify the sentence.

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一般来说，我们只会截断句子
In general, we only truncate sentences when they are longer

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超过模型可以处理的最大长度的。
than the maximum length the model can handle.

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用于填充第二句的值
The value used to pad the second sentence

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不应被随意挑选。
should not be picked randomly.

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该模型已经使用特定的填充 ID 进行了预训练，
The model has been pretrained with a certain padding ID,

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你可以在 tokenizer.pad_token_id 中找到它。
which you can find in tokenizer.pad_token_id.

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现在我们已经填充了句子，
Now that we have padded our sentences,

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我们可以和他们一起做一批。
we can make a batch with them.

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如果我们分别将两个句子传递给模型
If we pass the two sentences to the model separately

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或并批在一起，但是，我们注意到
or batched together, however, we notice

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我们没有得到相同的结果
that we don't get the same results

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对于被填充的句子。
for the sentence that is padded.

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在这里，第二个。
Here, the second one.

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希望是 transformer 库中的单词？
Expect the word in the transformer library?

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不。
No.

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如果你还记得 Transformer 模型大量使用
If you remember that Transformer models make heavy use

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注意力层，它应该不足为奇。
of attention layers, it should not come as a total surprise.

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在计算每个分词的上下文表示时，
When computing the contextual representation of each token,

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注意层查看所有其他词
the attention layers look at all the other words

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在句子中。
in the sentence.

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如果我们只有一句话
If we have just a sentence

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或者添加了几个填充 token 的句子，
or the sentence with several padding tokens added,

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我们没有得到相同的值是合乎逻辑的。
it's logical we don't get the same values.

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要在有或没有填充的情况下获得相同的结果，
To get the same results with or without padding,

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我们需要向注意力层表明
we need to indicate to the attention layers

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他们应该忽略那些填充 token 。
that they should ignore those padding tokens.

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这是通过创建一个注意力掩码来完成的，
This is done by creating an attention mask,

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与输入 ID 具有相同形状的 tensor
a tensor with the same shape as the input IDs

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用 0 和 1 。
with zeros and ones.

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1 的 token 表示注意层
Ones indicate the tokens the attention layers

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应该在上下文中考虑，
should consider in the context,

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并且 0 的 token 他们应该忽略。
and zeros, the tokens they should ignore.

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现在，通过这个注意力掩码
Now, passing this attention mask

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连同输入 ID 会给我们相同的结果
along with the input IDs will give us the same results

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就像我们将两个句子分别发送给模型一样。
as when we sent the two sentences individually to the model.

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这一切都是由分词器在幕后完成的
This is all done behind the scenes by the tokenizer

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当你将它应用于几个句子时
when you apply it to several sentences

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设置参数 padding=True。
with the flag padding=True.

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它将应用填充
It will apply the padding

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对较小的句子具有适当值
with the proper value to the smaller sentences

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并创建适当的注意掩码。
and create the appropriate attention mask.

