update text

assets/text.py

@@ -7,25 +7,25 @@ On this leaderboard, we share the evaluation results of LLMs obtained by develop
 # Dataset
 <span style="font-size:16px; font-family: 'Times New Roman', serif"> 
 To evaluate the safety risk of LLMs of large language models, we present ChineseSafe,  a Chinese safety benchmark to facilitate research 
-on the content safety of large language models for Chinese (Mandarin). 
+on the content safety of LLMs for Chinese (Mandarin). 
 To align with the regulations for Chinese Internet content moderation, our ChineseSafe contains 205,034 examples
 across 4 classes and 10 sub-classes of safety issues. For Chinese contexts, we add several special types of illegal content: political sensitivity, pornography,
 and variant/homophonic words. In particular, the benchmark is constructed as a balanced dataset, containing safe and unsafe data collected from internet resources and public datasets [1,2,3].
 We hope the evaluation can provides a guideline for developers and researchers to facilitate the safety of LLMs. <br>  
 
 The leadboard is under construction and maintained by <a href="https://hongxin001.github.io/" target="_blank">Hongxin Wei's</a> research group at SUSTech.
-We will release the technical report in the near future.
 Comments, issues, contributions, and collaborations are all welcomed!
 Email: weihx@sustech.edu.cn
 </span>
 """  # noqa
+# We will release the technical report in the near future.
 
 METRICS_TEXT = """
 # Metrics
 <span style="font-size:16px; font-family: 'Times New Roman', serif">
 We report the results with five metrics: overall accuracy, precision/recall for safe/unsafe content. 
 In particular, the results are shown as <b>metric/std</b> format in the table, 
-where <b>std</b> indicates the standard deviation of the results obtained from different random seeds.
+where <b>std</b> indicates the standard deviation of the results with various random seeds.
 </span>
  """ # noqa