Update

src/about.py

@@ -36,12 +36,21 @@ 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 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.  
-For fine-grained evaluation, please see our publications referenced below.
+For a fine-grained evaluation, please see our publications referenced below.
 
 ## Guardrail Models
-Guardrail models are Large Language Models fine-tuned for safety classification and employed to detect unsafe content in human-AI interactions.  
+Guardrail models are Large Language Models fine-tuned for safety classification, employed to detect unsafe content in human-AI interactions.  
 By complementing other safety measures such as safety alignment, they aim to prevent generative AI systems from providing harmful information to the users.
 
 ## GuardBench
@@ -56,6 +65,9 @@ Evaluation results are shown in terms of F1.
 We do not employ the Area Under the Precision-Recall Curve (AUPRC) as we found it overemphasizes models' Precision at the expense of Recall, thus hiding significant performance details.  
 We rely on [Scikit-Learn](https://scikit-learn.org/stable) to compute metric scores.
 
+## Fine-Grained Results
+Coming soon.
+
 ## Reproducibility
 Coming soon.
 """