Update README.md

README.md

@@ -11,10 +11,10 @@ datasets:
 - KnutJaegersberg/wikipedia_categories_labels
 ---
 
-This English model predicts the top 2 levels of the wikipedia categories (roundabout 1100 labels). It is trained on the concatenation of the headlines of the lower level categories articles in few shot setting (i.e. 8 subcategories with their headline concatenations per level 2 category). 
-Accuracy on test data split of the higher category level (37 labels) is 73 % and on level 2 is 60%. 
+This English model (e5-large as basis) predicts wikipedia categories (roundabout 37 labels). It is trained on the concatenation of the headlines of the lower level categories articles in few shot setting (i.e. 8 subcategories with their headline concatenations per level 2 category). 
+Accuracy on test data split is 85 %. 
 Note that these numbers are just an indicator that training worked, it will differ in production settings, which is why this classifier is meant for corpus exploration.  
-Use the wikipedia_categories_labels dataset as key. 
+Use the wikipedia_categories_labels dataset as key.
 
 
 
@@ -24,4 +24,4 @@ Download from Hub and run inference
 model = SetFitModel.from_pretrained("KnutJaegersberg/wikipedia_categories_setfit")
 
 Run inference
-preds = model(["Rachel Dolezal Faces Felony Charges For Welfare Fraud", "Elon Musk just got lucky", "The hype on AI is different from the hype on other tech topics"])
+preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])