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docs(zh-cn): Reviewed No. 08 - What happens inside the pipeline function? (PyTorch) #454

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docs(zh-cn): Reviewed No. 08 - What happens inside the pipeline function? (PyTorch) #454

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52 changes: 26 additions & 26 deletions subtitles/zh-CN/08_what-happens-inside-the-pipeline-function-(pytorch).srt
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@

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- 管道函数内部发生了什么？
- pipeline 函数内部发生了什么？
- What happens inside the pipeline function?

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Expand All @@ -25,22 +25,22 @@ of the Transformers library.

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更具体地说，我们将看看
详细来讲，我们将举例
More specifically, we will look

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

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以及它是如何从以下两个句子开始的，
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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00:00:22,020 --> 00:00:23,970
正负标签
将正负标签
to the positive and negative labels

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Expand All @@ -50,12 +50,12 @@ with their respective scores.

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正如我们在管道演示中看到的那样，
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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管道分为三个阶段。
pipeline 分为三个阶段。
there are three stages in the pipeline.

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Expand All @@ -65,7 +65,7 @@ First, we convert the raw texts to numbers

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该模型可以理解使用分词器。
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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Expand All @@ -75,17 +75,17 @@ Then those numbers go through the model,

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输出逻辑。
输出 logits 。
which outputs logits.

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

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那些登录到标签和分数。
那些 logits 包含标签和分数。
those logits into labels and scores.

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Expand All @@ -100,17 +100,17 @@ and how to replicate them using the Transformers library,

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从第一阶段开始，标记化。
从第一阶段开始，token 化。
beginning with the first stage, tokenization.

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令牌化过程有几个步骤。
token 化过程有几个步骤。
The tokenization process has several steps.

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首先，文本被分成称为标记的小块。
首先，文本被分成称为 token 的小块。
First, the text is split into small chunks called tokens.

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Expand All @@ -120,7 +120,7 @@ They can be words, parts of words or punctuation symbols.

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然后 tokenizer 将有一些特殊的标记，
然后 tokenizer 将有一些特殊的 token ，
Then the tokenizer will had some special tokens,

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Expand All @@ -130,17 +130,17 @@ if the model expect them.

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这里的模型在开头使用期望 CLS 令牌
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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00:01:16,860 --> 00:01:19,743
以及用于分类的句子末尾的 SEP 标记。
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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00:01:20,580 --> 00:01:24,180
最后，标记器将每个标记与其唯一 ID 匹配
最后，tokenizer 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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Expand Down Expand Up @@ -180,7 +180,7 @@ Here the checkpoint used by default

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00:01:45,360 --> 00:01:47,280
用于情绪分析管道
用于情绪分析的 pipeline
for the sentiment analysis pipeline

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Expand Down Expand Up @@ -250,7 +250,7 @@ Looking at the result, we see we have a dictionary

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用两把钥匙。
和两个主键
with two keys.

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Expand All @@ -265,7 +265,7 @@ with zero where the padding is applied.

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第二把钥匙，注意面具，
第二个键值，注意力 mask ，
The second key, attention mask,

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Expand All @@ -280,7 +280,7 @@ so the model does not pay attention to it.

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这就是标记化步骤中的全部内容。
这就是 token 化步骤中的全部内容。
This is all what is inside the tokenization step.

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Expand Down Expand Up @@ -350,7 +350,7 @@ for our classification problem.

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00:03:15,030 --> 00:03:19,230
这里的张量有两个句子，每个句子有 16 个标记，
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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Expand Down Expand Up @@ -425,12 +425,12 @@ This is because each model

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00:03:57,270 --> 00:04:00,810
每个模型都会返回 logits。
每个模型都会返回 logits 。
of the Transformers library returns logits.

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00:04:00,810 --> 00:04:02,250
为了理解这些逻辑，
为了理解这些 logits ，
To make sense of those logits,

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Expand Down Expand Up @@ -505,7 +505,7 @@ This is how our classifier built

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

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