docs(zh-cn): Reviewed 57_what-is-perplexity.srt (#401)

subtitles/zh-CN/57_what-is-perplexity.srt

@@ -15,22 +15,22 @@
 
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-- 在这段视频中，我们来看看
+- 在这段视频中，我们来了解一下
 - In this video, we take a look
 
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-在称为困惑度的神秘测深指标上。
+在称为困惑度的评估指标上。
 at the mysterious sounding metric called perplexity.
 
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-你可能遇到过困惑
+你在研究生成模型时
 You might have encountered perplexity
 
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-在阅读生成模型时。
+可能遇到过困惑度。
 when reading about generative models.
 
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@@ -40,7 +40,7 @@ You can see two examples here,
 
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-一张来自原始变压器纸，
+一个来自最初的 transformer 论文，
 one from the original transformer paper,
 
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@@ -50,17 +50,17 @@ one from the original transformer paper,
 
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-另一篇来自最近的 GPT-2 论文。
+另一个来自最近的 GPT-2 论文。
 and the other one from the more recent GPT-2 paper.
 
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-困惑度是衡量绩效的常用指标
+困惑度是衡量语言模型的性能
 Perplexity is a common metric to measure the performance
 
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-的语言模型。
+的常用指标。
 of language models.
 
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@@ -70,7 +70,7 @@ The smaller its value, the better the performance.
 
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-但它究竟意味着什么，我们又该如何计算呢？
+但它究竟意味着什么，我们又该如何计算得到它呢？
 But what does it actually mean and how can we calculate it?
 
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@@ -80,32 +80,32 @@ A very common quantity in machine learning
 
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-是可能性。
+是相似度。
 is the likelihood.
 
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-我们可以计算可能性
+我们可以计算似然性
 We can calculate the likelihood
 
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-作为每个标记概率的乘积。
+作为每个词元的概率的乘积。
 as the product of each token's probability.
 
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-这意味着对于每个令牌，
+这意味着对于每个词元，
 What this means is that for each token,
 
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-我们使用语言模型来预测它的概率
+我们使用语言模型基于之前的词元
 we use the language model to predict its probability
 
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-基于之前的标记。
+来预测它的概率。
 based on the previous tokens.
 
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@@ -115,12 +115,12 @@ In the end, we multiply all probabilities
 
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-得到的可能性。
+从而得到似然性。
 to get the likelihood.
 
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-有可能，
+通过似然性，
 With the likelihood,
 
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@@ -135,27 +135,27 @@ the cross-entropy.
 
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-你可能已经听说过交叉熵
+当你接触损失函数时
 You might have already heard about cross-entropy
 
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-在查看损失函数时。
+可能已经听说过交叉熵。
 when looking at loss function.
 
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-它通常用作分类中的损失函数。
+它通常在分类中作为损失函数使用。
 It is often used as a loss function in classification.
 
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-在语言建模中，我们预测下一个标记
+在语言建模中，我们基于之前的词元
 In language modeling, we predict the next token
 
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-基于之前的令牌，
+预测下一个词元，
 based on the previous token,
 
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@@ -185,32 +185,32 @@ with its inputs as labels.
 
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-然后损失对应于交叉熵。
+其损失与交叉熵相关。
 The loss then corresponds to the cross-entropy.
 
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-我们现在只差一个手术了
+现在对于计算困惑度
 We are now only a single operation away
 
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-从计算困惑度。
+我们现在只差一个操作了。
 from calculating the perplexity.
 
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-通过对交叉熵取幂，我们得到了困惑。
+通过对交叉熵取幂，我们得到了困惑度。
 By exponentiating the cross-entropy, we get the perplexity.
 
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-所以你看到困惑是密切相关的
+所以你可以发现困惑度和损失
 So you see that the perplexity is closely related
 
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-到损失。
+是密切相关的。
 to the loss.
 
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@@ -220,27 +220,27 @@ Plugging in previous results
 
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-表明这相当于求幂
+表明这相当于对每个词元
 shows that this is equivalent to exponentiating
 
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-每个令牌的负平均锁定概率。
+的负平均锁定概率取幂值。
 the negative average lock probability of each token.
 
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-请记住，损失只是一个弱代理
+请记住，损失只是针对模型
 Keep in mind that the loss is only a weak proxy
 
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-用于模型生成高质量文本的能力
+生成高质量文本的能力的一个弱代理
 for a model's ability to generate quality text
 
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-困惑也是如此。
+困惑度也是如此。
 and the same is true for perplexity.
 
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@@ -255,7 +255,7 @@ more sophisticated metrics
 
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-例如生成任务上的 BLEU 或 ROUGE。
+例如生成式任务上的 BLEU 或 ROUGE。
 such as BLEU or ROUGE on generative tasks.
 
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