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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import csv
import json
import re
import pandas as pd
def extract_model_answer(response):
if not response or "Internal Server Error" in response:
return "Internal Server Error"
# Look for the pattern "The final answer is <letter>"
match = re.search(r"The final answer is ([A-D])", response)
if match:
return match.group(1)
return ""
def process_answers(input_file, output_file):
# Read the JSONL file
data = []
with open(input_file, 'r') as f:
for line in f:
if line.strip(): # Skip empty lines
data.append(json.loads(line))
# Prepare CSV headers
headers = [
'Question',
'Choice A',
'Choice B',
'Choice C',
'Choice D',
'Expected Answer',
'Model Response',
'Extracted Model Answer',
]
# Write to CSV
with open(output_file, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(headers)
# Process each question
for question_data in data:
question = question_data.get('question', '')
choices = question_data.get('choices', {})
expected_answer = question_data.get('expected_answer', '')
model_response = question_data.get('model_response', '')
# Extract model answer
extracted_answer = extract_model_answer(model_response)
# Write row
row = [
question,
choices.get('A', ''),
choices.get('B', ''),
choices.get('C', ''),
choices.get('D', ''),
expected_answer,
model_response,
extracted_answer,
]
writer.writerow(row)
return output_file
def evaluate_results(csv_file, model_name):
# Read the CSV file
df = pd.read_csv(csv_file)
# Calculate metrics
total = len(df)
correct = len(df[df['Extracted Model Answer'] == df['Expected Answer']])
refusals = len(df[df['Extracted Model Answer'].str.contains('Internal Server Error', case=False, na=False)])
# Print results
print(f"\nModel: {model_name}")
print(f"Total problems: {total}")
print(f"Correct answers: {correct}")
print(f"Refusals: {refusals}")
print(f"Accuracy: {correct/total*100:.1f}% ({correct}/{total})")
def main():
# Set up argument parser
parser = argparse.ArgumentParser(description='Process and evaluate model responses')
parser.add_argument(
'--input_file', type=str, required=True, help='Path to the input JSONL file containing model responses'
)
parser.add_argument('--output_file', type=str, required=True, help='Path to the output CSV file')
parser.add_argument('--model_name', type=str, required=True, help='Name of the model for reporting results')
args = parser.parse_args()
# Process answers and generate CSV
print(f"Processing answers from {args.input_file}...")
csv_file = process_answers(args.input_file, args.output_file)
print(f"CSV file has been generated: {csv_file}")
# Evaluate results
print("\nEvaluating results...")
evaluate_results(csv_file, args.model_name)
if __name__ == "__main__":
main()
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