F string fix for nli and complex_reasoning

src/deepeval/complex_reasoning.py

@@ -26,6 +26,8 @@ class ComplexReasoningTask(BaseTask):
 
             # Get values from row
             choices = ast.literal_eval(row["choices"]) # Convert string to list
+            narrative = row["narrative"]
+            question = row["question"]
             formatted_choices = "\n".join([f"{chr(65+i)}: {choice}" for i, choice in enumerate(choices)])
             correct_answer_letter = row["answer_choice"]
             correct_answers.append(correct_answer_letter)
@@ -37,7 +39,7 @@ class ComplexReasoningTask(BaseTask):
 
             # Construct the prompt/message
             instruction = ""
-            prompt = f"Soru:\n{row["narrative"]}\n{row["question"]}\nSeçenekler:\n{formatted_choices}\n{instruction}\n"
+            prompt = f"Soru:\n{narrative}\n{question}\nSeçenekler:\n{formatted_choices}\n{instruction}\n"
             message = prompt
 
             # Get/format answer of the model

src/deepeval/nli.py

@@ -23,6 +23,9 @@ class NLITask(BaseTask):
             total_count += 1
 
             # Get values from row
+            text = row["text"]
+            premise = row["premise"]
+            hypothesis = row["hypothesis"]
             label = row["label"].lower().replace(' ','')
             choices=["entailment","contradiction","neutral"]
             formatted_choices = "\n".join([f"{chr(65+i)}: {choice}" for i, choice in enumerate(choices)])
@@ -40,8 +43,8 @@ class NLITask(BaseTask):
             # Construct the prompt/message
             instruction = ""
             question = "Yukarıdaki cümleler arasındaki ilişki “entailment” (bir cümle diğerini ima eder), “neutral (cümleler birbirini ima etmez ve çelişmez) veya “contradiction (cümleler birbirleriyle çelişir) olarak karakterize edilebilir. Bu ilişkilerden hangisi olduğunu söyleyin."
-            context = f"Bağlam:\n{row["text"]}\n" # can add to prompt if needed
-            prompt = f"Cümle1:\n{row["premise"]}\nCümle2:{row["hypothesis"]}\nSoru:\n{question}\nSeçenekler:\n{formatted_choices}\n{instruction}\n"
+            context = f"Bağlam:\n{text}\n" # can add to prompt if needed
+            prompt = f"Cümle1:\n{premise}\nCümle2:{hypothesis}\nSoru:\n{question}\nSeçenekler:\n{formatted_choices}\n{instruction}\n"
             message = prompt
 
             # Get/format answer of the model