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Merge pull request #3902 from myhloli/dev

Dev
Xiaomeng Zhao 1 month ago
parent
commit
e90a17a3d2
4 changed files with 6 additions and 6 deletions
  1. 1 1
      README.md
  2. 2 2
      README_zh-CN.md
  3. 1 1
      docs/en/quick_start/index.md
  4. 2 2
      docs/zh/quick_start/index.md

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README.md

@@ -684,7 +684,7 @@ A WebUI developed based on Gradio, with a simple interface and only core parsing
  
  
 <sup>1</sup> Accuracy metric is the End-to-End Evaluation Overall score of OmniDocBench (v1.5), tested on the latest `MinerU` version.   
 <sup>1</sup> Accuracy metric is the End-to-End Evaluation Overall score of OmniDocBench (v1.5), tested on the latest `MinerU` version.   
 <sup>2</sup> Linux supports only distributions released in 2019 or later.  
 <sup>2</sup> Linux supports only distributions released in 2019 or later.  
-<sup>3</sup> MLX requires macOS 13.5 or later, recommended for use with version 14.0 or higher.
+<sup>3</sup> MLX requires macOS 13.5 or later, recommended for use with version 14.0 or higher.  
 <sup>4</sup> Windows vLLM support via WSL2(Windows Subsystem for Linux).  
 <sup>4</sup> Windows vLLM support via WSL2(Windows Subsystem for Linux).  
 <sup>5</sup> Servers compatible with the OpenAI API, such as local or remote model services deployed via inference frameworks like `vLLM`, `SGLang`, or `LMDeploy`.
 <sup>5</sup> Servers compatible with the OpenAI API, such as local or remote model services deployed via inference frameworks like `vLLM`, `SGLang`, or `LMDeploy`.
 
 

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README_zh-CN.md

@@ -670,8 +670,8 @@ https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
 </table> 
 </table> 
 
 
 <sup>1</sup> 精度指标为OmniDocBench (v1.5)的End-to-End Evaluation Overall分数,基于`MinerU`最新版本测试  
 <sup>1</sup> 精度指标为OmniDocBench (v1.5)的End-to-End Evaluation Overall分数,基于`MinerU`最新版本测试  
-<sup>2</sup> Linux仅支持2019年及以后发行版
-<sup>3</sup> MLX需macOS 13.5及以上版本支持,推荐14.0以上版本使用
+<sup>2</sup> Linux仅支持2019年及以后发行版  
+<sup>3</sup> MLX需macOS 13.5及以上版本支持,推荐14.0以上版本使用  
 <sup>4</sup> Windows vLLM通过WSL2(适用于 Linux 的 Windows 子系统)实现支持  
 <sup>4</sup> Windows vLLM通过WSL2(适用于 Linux 的 Windows 子系统)实现支持  
 <sup>5</sup> 兼容OpenAI API的服务器,如通过`vLLM`/`SGLang`/`LMDeploy`等推理框架部署的本地模型服务器或远程模型服务
 <sup>5</sup> 兼容OpenAI API的服务器,如通过`vLLM`/`SGLang`/`LMDeploy`等推理框架部署的本地模型服务器或远程模型服务
 
 

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docs/en/quick_start/index.md

@@ -31,7 +31,7 @@ A WebUI developed based on Gradio, with a simple interface and only core parsing
         <tr>
         <tr>
             <th rowspan="2">Parsing Backend</th>
             <th rowspan="2">Parsing Backend</th>
             <th rowspan="2">pipeline <br> (Accuracy<sup>1</sup> 82+)</th>
             <th rowspan="2">pipeline <br> (Accuracy<sup>1</sup> 82+)</th>
-            <th colspan="4">vlm (Accuracy<sup>1</sup> 90+)</th>
+            <th colspan="4" style="text-align:center;">vlm (Accuracy<sup>1</sup> 90+)</th>
         </tr>
         </tr>
         <tr>
         <tr>
             <th>transformers</th>
             <th>transformers</th>

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docs/zh/quick_start/index.md

@@ -26,12 +26,12 @@
 >
 >
 > 在非主线环境中,由于硬件、软件配置的多样性,以及第三方依赖项的兼容性问题,我们无法100%保证项目的完全可用性。因此,对于希望在非推荐环境中使用本项目的用户,我们建议先仔细阅读文档以及FAQ,大多数问题已经在FAQ中有对应的解决方案,除此之外我们鼓励社区反馈问题,以便我们能够逐步扩大支持范围。
 > 在非主线环境中,由于硬件、软件配置的多样性,以及第三方依赖项的兼容性问题,我们无法100%保证项目的完全可用性。因此,对于希望在非推荐环境中使用本项目的用户,我们建议先仔细阅读文档以及FAQ,大多数问题已经在FAQ中有对应的解决方案,除此之外我们鼓励社区反馈问题,以便我们能够逐步扩大支持范围。
 
 
-<table>
+<table border="1">
     <thead>
     <thead>
         <tr>
         <tr>
             <th rowspan="2">解析后端</th>
             <th rowspan="2">解析后端</th>
             <th rowspan="2">pipeline <br> (精度<sup>1</sup> 82+)</th>
             <th rowspan="2">pipeline <br> (精度<sup>1</sup> 82+)</th>
-            <th colspan="4">vlm (精度<sup>1</sup> 90+)</th>
+            <th colspan="4" style="text-align:center;">vlm (精度<sup>1</sup> 90+)</th>
         </tr>
         </tr>
         <tr>
         <tr>
             <th>transformers</th>
             <th>transformers</th>