Update README.md

README.md

@@ -30,13 +30,13 @@ RoGuard 1.0, a SOTA instruction fine-tuned LLM, is designed to help safeguard ou
 
 
 ## 📊 Model Benchmark Results
-We benchmark RoGuard 1.0 model on a comprehensive set of open-source datasets for both prompt and response, as well as on RoGuard-Eval. This allows us to evaluate our model on both in-domain and out-of-domain datasets. We report our results in terms of F-1 score for binary violating/non-violating classification. In the table below, we compare our performance with that of several well-known models. The RoGuard 1.0 outperforms other models while generalizing on out-of-domain datasets.
 
-- **Prompt Metrics**: These evaluate how well the model classifies or responds to potentially harmful **user inputs**
-- **Response Metrics**: These measure how well the model handles or generates **responses**, ensuring its outputs are safe and aligned.
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/667c4d7fb4244f6997563346/iqmZ9bZvvluBEqZiAJ7_0.jpeg)
 
+We benchmark RoGuard 1.0 model on a comprehensive set of open-source datasets for both prompt and response, as well as on RoGuard-Eval. This allows us to evaluate our model on both in-domain and out-of-domain datasets. We report our results in terms of F-1 score for binary violating/non-violating classification. In the table above, we compare our performance with that of several well-known models. The RoGuard 1.0 outperforms other models while generalizing on out-of-domain datasets.
 
-![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/667c4d7fb4244f6997563346/iqmZ9bZvvluBEqZiAJ7_0.jpeg)
+- **Prompt Metrics**: These evaluate how well the model classifies or responds to potentially harmful **user inputs**
+- **Response Metrics**: These measure how well the model handles or generates **responses**, ensuring its outputs are safe and aligned.
 
 
 ## 🔗 GitHub Repository