Update README.md

README.md

@@ -316,7 +316,7 @@ def n_tokens(messages):
     """Count tokens in messages."""
     return sum([len(enc.encode(m["content"])) for m in messages])
 
-# Evaluate your model
+# Evaluate your model (Recommnd Using below AUC/weighted score )
 results = []
 for index, row in dataset.iterrows():
     messages = json.loads(row["prompt"])
@@ -365,9 +365,11 @@ if 'run_id' in results_df.columns:
     print("\n=== Experiment accuracy averaged across runs (run_id) ===")
     for _, r in exp_avg.iterrows():
         print(f"{r['experiment']}: {r['accuracy_percent']:.1f}% (averaged over runs)")
+```
 
+## 🏆 Advanced Evaluation with AUC Scoring (Highly Recommand)
+```python
 
-## 🏆 Advanced Evaluation with AUC Scoring
 
 ### Why AUC Scoring?
 - **Average accuracy** treats all tasks equally → poor model differentiation
@@ -376,7 +378,7 @@ if 'run_id' in results_df.columns:
 
 ### Complete Evaluation Function
 
-```python
+
 import math
 
 def compute_pi_auc_score(results, log_base=1.5):
@@ -417,11 +419,11 @@ def compute_pi_auc_score(results, log_base=1.5):
         'auc_log1.5_hard': wmean(hard) if hard else 0.0,
         'total_samples': len(results),
     }
-```
+
 
 ### Usage Example
 
-```python
+
 from datasets import load_dataset
 
 # Load PI-LLM dataset
@@ -446,23 +448,23 @@ print(f"🏆 AUC Score: {scores['auc_log1.5']:.3f}")  # PRIMARY metric
 if 'auc_log1.5_easy' in scores:
     print(f"📊 Easy Mode: {scores['auc_log1.5_easy']:.3f}")
     print(f"📊 Hard Mode: {scores['auc_log1.5_hard']:.3f}")
-```
+
 
 ### Output Formats
 
 **Single-Mode Experiments** (`exp_updates`, `exp_sequential`):
-```python
+
 {'avg_accuracy': 0.600, 'auc_log1.5': 0.412, 'total_samples': 100}
-```
+
 
 **Two-Mode Experiments** (`exp_keys`, `exp_valuelength`):  
-```python
+
 {
     'avg_accuracy': 0.600, 'auc_log1.5': 0.576,     # Overall metrics
     'auc_log1.5_easy': 0.850, 'auc_log1.5_hard': 0.350,  # Mode breakdown
     'total_samples': 150
 }
-```
+
 
 ### 🎯 For Model Ranking: Use `auc_log1.5` as your primary metric!
 
@@ -470,7 +472,6 @@ if 'auc_log1.5_easy' in scores:
 
 **Definition:** average of each test’s `auc_log1.5` (simple, clear leaderboard number).
 
-```python
 def compute_total_pi_auc(all_tests, log_base=1.5):
     """
     Total PI-AUC1.5 across tests = average of per-test auc_log1.5.
@@ -487,7 +488,7 @@ def compute_total_pi_auc(all_tests, log_base=1.5):
     total = sum(per_test.values()) / len(per_test) if per_test else 0.0
     return {"per_test_auc_log1.5": per_test, "total_auc_log1.5": total}
 
-
+```
 
 
 ## References