Update README.md

README.md

@@ -134,8 +134,8 @@ The model can be used for classification into topic labels from the
  applied to any news text in a language, supported by the `xlm-roberta-large`.
 
 Based on a manually-annotated test set (in Croatian, Slovenian, Catalan and Greek),
-the model achieves accuracy of 0.84 and macro-F1 scores of 0.78 on instances, predicted with confidence level above 0.90 (80% of test set).
-If we do not filter out instances, predicted with lower confidence score, the model achieves still a high accuracy of 0.78 and macro-F1 score of 0.72.
+the model achieves micro-F1 score of 0.734, macro-F1 score of 0.746 and accuracy of 0.734,
+and outperforms the GPT-4o model (version `gpt-4o-2024-05-13`) used in a zero-shot setting. 
 
 ## Intended use and limitations