48_inside-the-question-answering-pipeline-(tensorflow).srt

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- 让我们来看看问答 pipeline(管线) 的内部情况。
- Let's have a look inside the question answering pipeline.

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问答管线
The question answering pipeline

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可以提取问题的答案
can extract answers to questions

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来自给定的上下文或文本段落
from a given context or passage of text

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就像 transformer 仓库的 README 文件的这一部分。
like this part of the Transformers repo README.

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它也适用于很长的上下文，
It also works for very long context,

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即使答案靠后，就像这个例子一样。
even if the answer is at the very end, like in this example.

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在本视频中，我们将了解原因。
In this video, we'll see why.

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问答管线
The question answering pipeline

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遵循与其他管线相同的步骤。
follows the same steps as the other pipelines.

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问题和上下文被标记为一个句子对，
The question and context are tokenized as a sentence pair,

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提供给模型，然后应用一些后处理。
fed to the model then some post-processing is applied.

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所以分词化和模型步骤应该很熟悉。
So tokenization and model steps should be familiar.

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我们使用适合问答的 auto 类
We use the auto class suitable for question answering

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而不是序列分类，
instead of sequence classification,

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但与文本分类的一个关键区别
but one key difference with text classification

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是我们的模型输出两个张量
is that our model outputs two tensors

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命名 start logits 和 end logits。
named start logits and end logits.

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这是为什么？
Why is that?

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嗯，这就是模型找到答案的方式
Well, this is the way the model finds the answer

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对这个问题。
to the question.

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首先，让我们看一下模型输入。
First, let's have a look at the model inputs.

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它是与问题分词化相关的数字
It's numbers associated with the tokenization of the question

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,
,

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对于该上下文
followed by the context

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使用通常的 CLS 和 SEP 特殊 token 。
with the usual CLS and SEP special tokens.

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答案是那些 token 的一部分。
The answer is a part of those tokens.

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所以我们要求模型预测
So we ask the model to predict

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哪个 token 开始回答
which token starts the answer

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并结束答案。
and which ends the answer.

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对于我们的两个 logit 输出，
For our two logit outputs,

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理论标签是粉色和紫色的向量。
the theoretical labels are the pink and purple vectors.

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要将这些 logits 转换为概率，
To convert those logits into probabilities,

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我们需要应用 SoftMax，
we will need to apply a SoftMax,

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就像在文本分类管线中一样。
like in the text classification pipeline.

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我们只是掩蔽 token 
We just mask the tokens

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在这样做之前不属于上下文的一部分，
that are not part of the context before doing that,

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不掩蔽初始 CLS token 
leaving the initial CLS token unmasked

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我们用它来预测一个不可能的答案。
as we use it to predict an impossible answer.

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这就是它在代码方面的样子。
This is what it looks like in terms of code.

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我们使用一个大的负数作为掩码
We use a large negative number for the masking

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因为它的指数将为零。
since its exponential will then be zero.

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现在每个开始和结束位置的概率
Now the probability for each start and end position

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对应一个可能的答案
corresponding to a possible answer

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会给一个分数是一个产品
will give a score that is a product

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开始概率和结束概率
of the start probabilities and end probabilities 

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在这些位置。
at those position.

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当然，开始索引大于结束索引
Of course, a start index greater than an end index

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对应一个不可能的答案。
corresponds to an impossible answer.

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这是找到最佳分数的代码
Here is the code to find the best score

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对一个可能的答案。
for a possible answer.

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一旦我们有了 token 的开始和结束位置，
Once we have the start and end position for the tokens,

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我们使用分词器提供的偏移量映射
we use the offset mappings provided by our tokenizer

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找到初始上下文中的字符范围，
to find the span of characters in the initial context,

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我们得到了答案。
and we get our answer.

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现在，当上下文很长时，
Now, when the context is long,

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它可能会被分词器截断。
it might get truncated by the tokenizer.

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这可能会导致部分答案，或者更糟的是，
This might result in part of the answer, or worse,

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整个答案，被截断了。
the whole answer, being truncated.

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所以我们不丢弃截断的 token 
So we don't discard the truncated tokens

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但与他们一起构建新功能。
but build new features with them.

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这些功能中的每一个都包含问题，
Each of those features contains the question,

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然后是上下文中的一大块文本。
then a chunk of text in the context.

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如果我们采用不相交的文本块，
If we take disjoint chunks of texts,

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我们可能会得到答案
we might end up with the answer

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被分成两个特征。
being split between two features.

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因此，我们取而代之的是重叠的文本块
So instead, we take overlapping chunks of text

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确保至少其中一个块
to make sure at least one of the chunks

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将完整包含问题的答案。
will fully contain the answer to the question.

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所以，分词器会自动为我们完成所有这些
So, tokenizers does all of this for us automatically

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使用 return overflowing tokens 选项。
with the return overflowing tokens option.

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步幅参数
The stride argument

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控制重叠标记的数量。
controls the number of overlapping tokens.

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这是我们很长的上下文
Here is how our very long context

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在一些重叠的两个特征中被截断。
gets truncated in two features with some overlap.

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通过应用相同的后处理
By applying the same post-processing

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我们之前看到的每个功能，
we saw before for each feature,

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我们得到每个分数的答案，
we get the answer with a score for each of them,

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我们选择得分最高的答案
and we take the answer with the best score

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作为最终解决方案。
as a final solution.

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