lobehubbot
commented
7 months ago Bot detected the issue body's language is not English, translate it automatically. 👯👭🏻🧑🤝🧑👫🧑🏿🤝🧑🏻👩🏾🤝👨🏿👬🏿
💻 System environment
macOS
📦 Deployment environment
Docker
🌐 Browser
Chrome
🐛 Problem description
The rendering of the list is actually different!
yarn dev started:
Deployed by docker:
🚦 Expected results
I hope they are consistent and can solve https://github.com/lobehub/lobe-chat/issues/1161
I originally planned to try to modify it locally, but I accidentally discovered this problem and I am not sure why.
📷 Steps to reproduce
Test text:
In information theory, "entropy", "surprise" and "cross-entropy" are important concepts for measuring the amount of information.
- **Entropy** (Entropy) is a measure of the uncertainty of a random variable, defined as the probability of all possible values of the random variable multiplied by the expectation of the negative logarithm of it. For discrete random variable $X$, its entropy $H(X)$ can be expressed as: $$H(X) = -\sum_{i} p(x_i)\log p(x_i)$$where, $p( x_i)$ is the probability that the random variable $X$ takes a specific value $x_i$.
- **Surprise** (Surprisal or Information Content) refers to the "surprise" or "amount of information" brought by this information when receiving a specific information. Its size is proportional to the negative logarithm of the probability of the information occurring. . Surprise $I(x_i)$ for event $x_i$ is defined as: $$I(x_i) = -\log p(x_i)$$where $p(x_i)$ is the probability of event $x_i$ occurring. High surprise means that the probability of the event occurring is low and therefore the amount of information is high.
- **Cross-Entropy** (Cross-Entropy) is a measure of the difference between two probability distributions, especially in machine learning, used to measure the difference between the actual output distribution and the expected output distribution. For discrete random variables, the cross entropy $H(p, q)$ between two probability distributions $p(x)$ and $q(x)$ is defined as: $$H(p, q) = -\sum_ {i} p(x_i)\log q(x_i)$$where $p(x_i)$ is the probability of event $x_i$ occurring in a true probability distribution, and $q(x_i)$ is another probability distribution (e.g. , the probability of occurrence of event $x_i$ in the distribution predicted by the model).
In summary, surprise is a measure of the amount of information received when a particular piece of information is received, the magnitude of which is proportional to the negative logarithm of the probability of that information occurring.📝 Supplementary information
I used git reset --hard origin/main to synchronize to the latest commit.
zhuozhiyongde
arvinxx
💻 系统环境
macOS
📦 部署环境
Docker
🌐 浏览器
Chrome
🐛 问题描述
对于列表的渲染居然不一样！
yarn dev 启动的：
docker 部署的：
🚦 期望结果
我期望他们一致，且能解决 https://github.com/lobehub/lobe-chat/issues/1161
我本来打算本地尝试修改，但是偶然间发现了这个问题，不是很清楚原因。
https://github.com/lobehub/lobe-chat/commit/be7554957e69e7d6218de37d87d169760956b5bb#commitcomment-139863071
📷 复现步骤
测试文本：
📝 补充信息
我使用 git reset --hard origin/main 已经同步到最新 commit.