Update

src/about.py

@@ -36,17 +36,17 @@ INTRODUCTION_TEXT = """"""
 LLM_BENCHMARKS_TEXT = f"""
 ## GuardBench Leaderboard
 
-Welcome to the GuardBench Leaderboard, an independent benchmark designed to evaluate guardrail models.  
+Welcome to the **GuardBench's Leaderboard**, an *independent* benchmark designed to evaluate guardrail models.  
 
 The leaderboard reports results for the following datasets:
-- PromptsEN: 30k+ English prompts
-- ResponsesEN: 33k+ English single-turn conversations where the AI-generated response may be safe or unsafe
-- PromptsDE 30k+ German prompts
-- PromptsFR: 30k+ French prompts
-- PromptsIT: 30k+ Italian prompts
-- PromptsES: 30k+ Spanish prompts
-
-Evaluation results are shown in terms of F1.  
+- **PromptsEN**: 30k+ English prompts compiled from multiple sources
+- **ResponsesEN**: 33k+ English single-turn conversations from multiple sources where the AI-generated response may be safe or unsafe
+- **PromptsDE** 30k+ German prompts
+- **PromptsFR**: 30k+ French prompts
+- **PromptsIT**: 30k+ Italian prompts
+- **PromptsES**: 30k+ Spanish prompts
+
+Evaluation **results** are shown in terms of **F1**.  
 For a fine-grained evaluation, please see our publications referenced below.
 
 ## Guardrail Models
@@ -54,11 +54,11 @@ Guardrail models are Large Language Models fine-tuned for safety classification,
 By complementing other safety measures such as safety alignment, they aim to prevent generative AI systems from providing harmful information to the users.
 
 ## GuardBench
-GuardBench is a large-scale benchmark for guardrail models comprising 40 safety evaluation datasets that was recently proposed to evaluate their effectiveness.  
+GuardBench is a large-scale benchmark for guardrail models comprising *40 safety evaluation datasets* that was recently proposed to evaluate their effectiveness.  
 You can find more information in the [paper](https://aclanthology.org/2024.emnlp-main.1022) we presented at EMNLP 2024.
 
 ## Python
-GuardBench is accompained by a [Python library](https://github.com/AmenRa/GuardBench) providing evaluation functionalities on top of it.
+GuardBench is supported by a [Python library](https://github.com/AmenRa/GuardBench) providing evaluation functionalities on top of it.
 
 ## Evaluation Metric
 Evaluation results are shown in terms of F1.