Update custom_models/groundedness_checker/main.py

custom_models/groundedness_checker/main.py

@@ -1,47 +1,49 @@
-
-from llmgaurdrails.custom_models.groundedness_checker.pdf_data_chunker import process_pdf
-from llmgaurdrails.custom_models.groundedness_checker.llm_based_qa_generator import  LLMBasedQAGenerator
-from llmgaurdrails.custom_models.groundedness_checker.grounding_classifier import GroundingTrainer
-from llmgaurdrails.custom_models.groundedness_checker.simple_qa_generator import SimpleQAGenerator
-from llmgaurdrails.custom_models.groundedness_checker.evaluate_groundedness_model import evaluate,get_eval_data
-
-# Usage
-if __name__ == "__main__":
-    
-    # pdf_path =  # Replace with your PDF
-    trainning_pdf_paths = ["D:\Sasidhar\Projects\cba\data\CreditCard.pdf" ,
-                 "D:\Sasidhar\Projects\cba\data\home_insurance_pds.pdf"]
-    
-    eval_pdf_paths = ["D:\Sasidhar\Projects\llm_gaurdrails\llmgaurdrails\data\CreditCard.pdf"]
-    
-    all_chunks = []
-
-    for path in trainning_pdf_paths:
-        chunks = process_pdf(trainning_pdf_paths[0])
-        all_chunks.append(chunks)
-
-    chunks_flattened = [x for xs in all_chunks for x in xs]
-    
-    # generate qa dataset 
-    qa_generator = LLMBasedQAGenerator()
-
-    dataset = qa_generator.generate_dataset(chunks_flattened,persist_dataset=True)
-
-    trainer = GroundingTrainer()
-    trainer.train(dataset)
-
-    eval_dataset = get_eval_data(eval_pdf_paths=eval_pdf_paths)
-    evaluate(dataset)
-    # Accuracy: 0.8952380952380953
-    # Precision: 0.8738738738738738
-    # Recall: 0.9238095238095239
-    # F1 Score: 0.8981481481481481
-
-
-
-    
-    
-  
-    
-
+
+from llmgaurdrails.custom_models.groundedness_checker.pdf_data_chunker import process_pdf
+from llmgaurdrails.custom_models.groundedness_checker.llm_based_qa_generator import  LLMBasedQAGenerator
+from llmgaurdrails.custom_models.groundedness_checker.grounding_classifier import GroundingTrainer
+from llmgaurdrails.custom_models.groundedness_checker.simple_qa_generator import SimpleQAGenerator
+from llmgaurdrails.custom_models.groundedness_checker.evaluate_groundedness_model import evaluate,get_eval_data
+
+if __name__ == "__main__":
+    
+    # Replace with your PDF Files
+    trainning_pdf_paths = ["D:\Sasidhar\Projects\cba\data\CreditCard.pdf" ,
+                 "D:\Sasidhar\Projects\cba\data\home_insurance_pds.pdf"]
+    
+    eval_pdf_paths = ["D:\Sasidhar\Projects\llm_gaurdrails\llmgaurdrails\data\PrivateBanking.pdf"]
+    
+    all_chunks = []
+
+    for path in trainning_pdf_paths:
+        chunks = process_pdf(trainning_pdf_paths[0])
+        all_chunks.append(chunks)
+
+    chunks_flattened = [x for xs in all_chunks for x in xs]
+    
+    # generate qa dataset 
+    qa_generator = LLMBasedQAGenerator()
+
+    dataset = qa_generator.generate_dataset(chunks_flattened,persist_dataset=True)
+
+    trainer = GroundingTrainer()
+    trainer.train(dataset)
+
+    eval_dataset = get_eval_data(eval_pdf_paths=eval_pdf_paths)
+    evaluate(dataset)
+
+    # Result on test dataset - This is wrong as these numbers were obtained on a trained dataset by mistake  . Will fix before the presentation.
+    
+    # Accuracy: 0.8952380952380953
+    # Precision: 0.8738738738738738
+    # Recall: 0.9238095238095239
+    # F1 Score: 0.8981481481481481
+
+
+
+    
+    
+  
+    
+