Update README.md

README.md

@@ -115,8 +115,9 @@ The model can be used for classification into topic labels from the
 [IPTC NewsCodes schema](https://iptc.org/std/NewsCodes/guidelines/#_what_are_the_iptc_newscodes) and can be
  applied to any news text in a language, supported by the `xlm-roberta-large`.
 
-Based on out multilingual manually-annotated test set (in Croatian, Slovenian, Catalan and Greek),
-the model achieves accuracy of 0.836 and macro-F1 scores of 0.778 on instances, predicted with confidence level above 0.90.
+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.
 
 ## Intended use and limitations