Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update perplexity.md #929

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update perplexity.md #929

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion docs/tutorials/deep_learning/metrics/perplexity.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ p(s) &= p(w_1,w_2,...,w_n) \\ &= \prod_{i=1}^n p(w_i|w_1,w_2,...,w_{i-1})
\end{align}
$$

在语言模型训练完成之后，如何去评判语言模型的好坏？这时，困惑度就可以发挥作用了。一般来讲，用于评判语言模型的测试集均是合理的、高质量的语料，只要语言模型在测试集上的困惑度越高，则代表语言模型训练地越好，反之亦然。
在语言模型训练完成之后，如何去评判语言模型的好坏？这时，困惑度就可以发挥作用了。一般来讲，用于评判语言模型的测试集均是合理的、高质量的语料，只要语言模型在测试集上的困惑度越低，则代表语言模型训练地越好，反之亦然。

在了解了语句概率的计算后，则对于语句$s=w_1, w_2, w_3, ..., w_n$​​，其困惑度可以这样来定义：

Expand Down