Update README.md

README.md

@@ -59,4 +59,6 @@ print(f"Generated raw text:\n{generated_text}\n\n")
 ```
 
 # Model performance
-This model achieves an MAE of 0.33 on the ramdomly splitted test set from the Materials Project. The performance is comparable to the state-of-the-art models for bandgap prediction.
+This model delivers an MAE of 0.33 on the test set, which was randomly split from the Materials Project dataset, with 113,568 instances for training and 12,618 for testing. 
+The results show promise as a baseline for materials property prediction models based on Large Language Models. 
+For comparison, one of the current state-of-the-art models, [CrystalFormer](https://omron-sinicx.github.io/crystalformer/), achieves an MAE of around 0.20 eV using 60k training data from the Materials Project dataset.