Update README.md

README.md

@@ -63,9 +63,15 @@ Additional information about the datasets will be included in the Meditron-3 pub
 | Model Name                  | MedmcQA | MedQA  | PubmedQA | Average |
 |-----------------------------|---------|--------|----------|---------|
 | google/gemma-2-9b           | 56.60   | 63.32  | 76.80    | 65.57   |
-| gemMeditron-2-9b-4818       | 57.21   | 63.79  | 77.00    | 66.00   |
+| gemMeditron-2-9b            | 57.21   | 63.79  | 77.00    | 66.00   |
 | Difference (gemMeditron vs.)| 0.61    | 0.47   | 0.20     | 0.43    |
 
+| Model Name                  | AfrimedQA |
+|-----------------------------|-----------|
+| google/gemma-2-9b           | 51.25     |
+| gemMeditron-2-9b            | 58.23     |
+| Difference (gemMeditron vs.)| 6.98      |
+
 
 We evaluated Meditron on medical multiple-choice questions using [lm-harness](https://github.com/EleutherAI/lm-evaluation-harness) for reproducibility.
 While MCQs are valuable for assessing exam-like performance, they fall short of capturing the model's real-world utility, especially in terms of contextual adaptation in under-represented settings. Medicine is not multiple choice and we need to go beyond accuracy to assess finer-grained issues like empathy, alignment to local guidelines, structure, completeness and safety. To address this, we have developed a platform to collect feedback directly from experts to continuously adapt to the changing contexts of clinical practice.