Update README.md

README.md

@@ -23,15 +23,27 @@ Built from a limited set of English, Swedish and Norwegian titles to classify ne
 
 The model has been fine-tuned on a dataset that is greatly skewed, but has been slightly augmented to stabilize it.
 
+## Model description
+
+The model is intended to categorize Norwegian, Swedish and English news content within the specified 16 categories but is a test model for demonstration purposes. 
+It needs more data within several categories to provide 100% value but it will outperform Claude Haiku and GPT-3.5 on this use case.
+
+## Intended uses & limitations
+
+Use it to categorize news texts. Only set the category if the value is at least 60% for the label, otherwise the model is uncertain.
+
 # Test examples 
 
 **Input:** Mann siktet for drapsforsøk på Slovakias statsministeren
+
 **Output:** crime, law and justice
 
 **Input:** Tre døde i kioskbrann i Tyskland
-Output: disaster, accident, and emergency incident
+
+**Output:** disaster, accident, and emergency incident
 
 **Input:** Kultfilm får Netflix-oppfølger. Kultfilmen «Happy Gilmore» fra 1996 får en oppfølger på Netflix. Det røper strømmetjenesten selv på X, tidligere Twitter. –Happy Gilmore er tilbake!
+
 **Output:** arts, culture, entertainment and media
 
 # Performance
@@ -61,15 +73,6 @@ See the performance (accuracy) for each label below:
 - Weather: 1.0
 - Religion: 0.0
 
-## Model description
-
-The model is intended to categorize Norwegian, Swedish and English news content within the specified 16 categories but is a test model for demonstration purposes. 
-It needs more data within several categories to provide 100% value but it will outperform Claude Haiku and GPT-3.5 on this use case.
-
-## Intended uses & limitations
-
-Use it to categorize news texts. Only set the category if the value is at least 60% for the label, otherwise the model is uncertain.
-
 ## Training and evaluation data
 
 Trained with the trainer, setting a learning rate of 2e-05 and batch size of 16 for 3 epochs.