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README.md

@@ -141,14 +141,13 @@ Parameters of the fit()-Method:
         "lr": 4e-05
     },
     "scheduler": "WarmupLinear",
-    "warmup_steps": 66,
-    "weight_decay": 0.06
+    "warmup_steps": 66
 }
 ```
 
 ## Evaluation
 
-We applied a 6-fold (In-Topic) cross-validation method to demonstrate WRAP's optimal performance. This involved using the same dataset and parameters 
+We applied a 6-fold (Closed-Topic) cross-validation method to demonstrate WRAP's optimal performance. This involved using the same dataset and parameters 
 described in the *Training* section, where we trained on k-1 splits and made predictions using the kth split.
 
 Additionally, we assessed its ability to generalize across the 6 topics (Cross-Topic) of TACO. Each of the k topics was utilized for testing, while 
@@ -156,19 +155,19 @@ the remaining k-1 topics were used for training purposes.
 
 In total, the WRAP classifier performs as follows:
 
-### Content Management
+### Binary Classification Tasks
 
-| Macro-F1    | Inference | Information | Multiclass |
-|-------------|-----------|-------------|------------|
-| In-Topic    | 87.71%    | 85.34%      | 75.80%     |
-| Cross-Topic | 86.71%    | 84.58%      | 73.92%     |
+| Macro-F1     | Inference | Information | Multi-Class |
+|--------------|-----------|-------------|-------------|
+| Closed-Topic | 86.62%    | 86.30%      | 75.29%      |
+| Cross-Topic  | 86.27%    | 84.90%      | 73.54%      |
 
-### Classification
+### Multi-Class Classification Task
 
-| Micro-F1    | Reason | Statement | Notification | None   |
-|-------------|--------|-----------|--------------|--------|
-| In-Topic    | 77.82% | 61.10%    | 80.56%       | 83.71% |
-| Cross-Topic | 76.52% | 58.99%    | 78.43%       | 81.73% |
+| Micro-F1     | Reason | Statement | Notification | None   |
+|--------------|--------|-----------|--------------|--------|
+| Closed-Topic | 78.14% | 60.96%    | 79.36%       | 82.72% |
+| Cross-Topic  | 77.05% | 58.33%    | 78.45%       | 80.33% |
 
 # Environmental Impact