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import argparse |
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import json |
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import os |
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import openai |
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import tqdm |
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import ray |
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import time |
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NUM_SECONDS_TO_SLEEP = 3 |
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@ray.remote(num_cpus=4) |
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def get_eval(content: str, max_tokens: int): |
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while True: |
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try: |
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response = openai.ChatCompletion.create( |
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model='gpt-4', |
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messages=[{ |
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'role': 'system', |
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'content': 'You are a helpful and precise assistant for checking the quality of the answer.' |
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}, { |
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'role': 'user', |
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'content': content, |
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}], |
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temperature=0.2, |
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max_tokens=max_tokens, |
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) |
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break |
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except openai.error.RateLimitError: |
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pass |
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except Exception as e: |
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print(e) |
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time.sleep(NUM_SECONDS_TO_SLEEP) |
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print('success!') |
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return response['choices'][0]['message']['content'] |
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def parse_score(review): |
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try: |
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score_pair = review.split('\n')[0] |
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score_pair = score_pair.replace(',', ' ') |
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sp = score_pair.split(' ') |
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if len(sp) == 2: |
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return [float(sp[0]), float(sp[1])] |
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else: |
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print('error', review) |
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return [-1, -1] |
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except Exception as e: |
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print(e) |
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print('error', review) |
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return [-1, -1] |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser(description='ChatGPT-based QA evaluation.') |
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parser.add_argument('-q', '--question') |
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parser.add_argument('-a', '--answer-list', nargs='+', default=[]) |
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parser.add_argument('-r', '--rule') |
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parser.add_argument('-o', '--output') |
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parser.add_argument('--max-tokens', type=int, default=1024, help='maximum number of tokens produced in the output') |
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args = parser.parse_args() |
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ray.init() |
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f_q = open(os.path.expanduser(args.question)) |
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f_ans1 = open(os.path.expanduser(args.answer_list[0])) |
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f_ans2 = open(os.path.expanduser(args.answer_list[1])) |
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rule_dict = json.load(open(os.path.expanduser(args.rule), 'r')) |
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review_file = open(f'{args.output}', 'w') |
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js_list = [] |
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handles = [] |
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idx = 0 |
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for ques_js, ans1_js, ans2_js in zip(f_q, f_ans1, f_ans2): |
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ques = json.loads(ques_js) |
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ans1 = json.loads(ans1_js) |
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ans2 = json.loads(ans2_js) |
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category = json.loads(ques_js)['category'] |
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if category in rule_dict: |
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rule = rule_dict[category] |
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else: |
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rule = rule_dict['default'] |
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prompt = rule['prompt'] |
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role = rule['role'] |
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content = (f'[Question]\n{ques["text"]}\n\n' |
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f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n' |
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f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n' |
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f'[System]\n{prompt}\n\n') |
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js_list.append({ |
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'id': idx+1, |
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'question_id': ques['question_id'], |
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'answer1_id': ans1['answer_id'], |
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'answer2_id': ans2['answer_id'], |
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'category': category}) |
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idx += 1 |
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handles.append(get_eval.remote(content, args.max_tokens)) |
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time.sleep(NUM_SECONDS_TO_SLEEP) |
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reviews = ray.get(handles) |
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for idx, review in enumerate(reviews): |
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scores = parse_score(review) |
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js_list[idx]['content'] = review |
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js_list[idx]['tuple'] = scores |
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review_file.write(json.dumps(js_list[idx]) + '\n') |
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review_file.close() |
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