--- a
+++ b/scripts/evaluate.py
@@ -0,0 +1,84 @@
+import json
+
+def parse_entities(record, key):
+    """
+    Parse entities from a record in the data.
+    
+    Args:
+    - record: A dictionary representing a single record in the data.
+    - key: The key to extract data from ('output' or 'prediction').
+
+    Returns:
+    - A set containing the extracted entities.
+    """
+    # Convert the string into a dictionary
+    entities = json.loads(record[key])
+    
+    # Initialize a set to store the entities
+    flattened_entities = set()
+    
+    # Extract the entities
+    for value in entities.values():
+        # Check if item is a list of adverse events
+        if isinstance(value, list):
+            flattened_entities.update(value)
+        # Parse drug names
+        else:
+            flattened_entities.add(value)
+    
+    return flattened_entities
+
+def calculate_precision_recall(data):
+    """
+    Calculate precision and recall from the data.
+
+    Args:
+    - data: A list of dictionaries, each containing 'output' and 'prediction'.
+
+    Returns:
+    - precision: The precision of the predictions.
+    - recall: The recall of the predictions.
+    """
+    # Initialize variables
+    true_positives = 0
+    false_positives = 0
+    false_negatives = 0
+    
+    # parse all the samples in the test dataset
+    for record in data:
+        # Extract ground truths
+        gt_entities = parse_entities(record, 'output')
+        # Extract predictions
+        pred_entities = parse_entities(record, 'prediction')
+    
+        # Calculate TP, FP, FN for each sample in test data
+        true_positives += len(gt_entities & pred_entities)
+        false_positives += len(pred_entities - gt_entities)
+        false_negatives += len(gt_entities - pred_entities)
+    
+    # Calculate Precision
+    precision = true_positives / (true_positives + false_positives) if true_positives + false_positives > 0 else 0
+    # Calculate Recall
+    recall = true_positives / (true_positives + false_negatives) if true_positives + false_negatives > 0 else 0
+
+    return precision, recall
+
+if __name__ == '__main__':
+
+    prediction_files = [
+                'data/predictions-llama2-adapter.json',   # Llama-2 Adapter
+                'data/predictions-stablelm-adapter.json', # Stable-LM Adapter
+                'data/predictions-llama2-lora.json',      # Llama-2 Lora
+                'data/predictions-stablelm-lora.json',    # Stable-LM Lora
+                ]
+    
+    for filename in prediction_files:
+        # Get model name and tune type
+        file_components = filename.split('-')
+        
+        # Load the predcitions JSON data
+        with open(filename, 'r') as file:
+            data = json.load(file)
+
+        precision, recall = calculate_precision_recall(data)
+        print(f"[INFO] {file_components[1]}-{file_components[2].split('.')[0]} ----> Precision: {round(precision,3)} Recall: {round(recall,3)}")
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