diff --git a/docs/Advanced/finetune.md b/docs/Advanced/finetune.md
index 162fd01..0c29ad7 100644
--- a/docs/Advanced/finetune.md
+++ b/docs/Advanced/finetune.md
@@ -12,7 +12,8 @@ Our baseline model provides robust performance across many use cases. However, f
    - Start by testing the baseline model on a representative subset of your data using the [Evaluation function](#performance-evaluation)
    - Collect performance metrics:
      - Accuracy rate
-     - Cohen’s Kappa
+     - F1 Score
+     - Recall
 
 2. Decision Criteria for Fine-Tuning
    - Consider fine-tuning if:
@@ -50,16 +51,6 @@ You can evaluate model performance using the `CALL MODEL_OPTIMIZATION.EVALUATE_P
 CALL MODEL_OPTIMIZATION.EVALUATE_PERFORMANCE();
 ```
 
-### **Interpretation of Kappa Score:**
-| **Kappa Score (κ)** | **Level of Agreement** |
-|----------------------|------------------------|
-| < 0.0               | Poor (Worse than chance) |
-| 0.0 – 0.20          | Slight agreement        |
-| 0.21 – 0.40         | Fair agreement          |
-| 0.41 – 0.60         | Moderate agreement      |
-| 0.61 – 0.80         | Substantial agreement   |
-| 0.81 – 1.00         | Almost perfect agreement |
-
 # Fine-tuning the Model
 
 If you want to increase the accuracy by tuning the model to your own network you can use the following commands.