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from evaluation_utils import * |
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from multiple_choice_generation import * |
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def get_model_mc_response(model_name,model_cache_dir,mc_dir,questions_file,response_file=None,temperature=1,top_p=0,gpt_azure=True): |
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if response_file == None: |
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response_file = f"{model_name}-mc_res.csv" |
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questions_df = pd.read_csv(os.path.join(mc_dir,questions_file),encoding='utf-8') |
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already = None |
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if not os.path.exists(os.path.join(mc_dir,response_file)): |
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write_csv_row(list(questions_df.columns)+['full_res','final_ans'],os.path.join(mc_dir,response_file)) |
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else: |
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already = pd.read_csv(os.path.join(mc_dir,response_file),encoding='utf-8') |
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tokenizer,model = get_tokenizer_model(model_name,MODEL_PATHS[model_name],model_cache_dir) |
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pb = tqdm(questions_df.iterrows(),total=len(questions_df)) |
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right = 0 |
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for i,row in pb: |
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qid = row['MCQID'] |
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pb.set_description(qid) |
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if isinstance(already,pd.DataFrame): |
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if qid in set(already['MCQID']): |
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continue |
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country = row['country'] |
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prompt = row['prompt'] |
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print(prompt) |
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full_res = get_model_response(model_name,prompt,model,tokenizer,temperature,top_p,gpt_azure) |
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print(full_res) |
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json_res = get_json_str(full_res) |
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if isinstance(json_res,dict) and 'answer_choice' in json_res: |
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try: |
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final_ans = re.findall(r'[A-Z]',str(json_res['answer_choice']))[0] |
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if final_ans+'.' not in prompt: |
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for k,v in json.loads(row['choices']).items(): |
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if v == json_res['answer_choice']: |
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final_ans = str(k) |
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break |
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else: |
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final_ans = full_res |
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except: |
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for k,v in json.loads(row['choices']).items(): |
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if v == json_res['answer_choice']: |
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final_ans = str(k) |
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break |
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else: |
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final_ans = full_res |
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else: |
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try: |
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final_ans = re.findall(r'[A-Z]',json_res)[0] |
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except: |
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final_ans = full_res |
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write_csv_row(list(row)+[full_res,final_ans],os.path.join(mc_dir,response_file)) |
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if final_ans == row['answer_idx']: |
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right += 1 |
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pb.set_postfix({'score':right/(i+1)}) |
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def multiple_choice_score(model,mc_dir,mrf,mc_res_file,eval_res_file,wrong_country_ratio_file,country): |
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df = pd.read_csv(os.path.join(mc_dir,mrf),encoding='utf-8') |
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df = df[df['country'] == country] |
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scores = [] |
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for i,row in tqdm(df.iterrows(),total=len(df)): |
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if str(row['answer_idx']) == str(row['final_ans']): |
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scores.append(1) |
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else: |
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scores.append(0) |
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df['score'] = scores |
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final_score = df['score'].mean() |
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return final_score |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description='Choose your model(s) & language(s)') |
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parser.add_argument('--model',type=str, |
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help='Provide the model you want to use. Check and choose from the key values of the MODEL_PATHS variable. If you want to test on multiple models, provide multiple model names with ", " between each (e.g., "gpt-4-0125-preview, aya-101").') |
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parser.add_argument('--model_cache_dir',type=str,default='.cache', |
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help='Provide the directory saving model caches.') |
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parser.add_argument('--mc_dir',type=str,default='./mc_data', |
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help='Provide the directory for the data files from the human annotators.') |
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parser.add_argument('--questions_file',type=str,default='mc_questions_file.csv', |
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help='Provide the directory for the data files from the human annotators.') |
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parser.add_argument('--response_file',type=str,default=None, |
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help='Provide the filename to save LLM responses.') |
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parser.add_argument('--temperature',type=int,default=0, |
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help='Provide generation temperature for LLMs.') |
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parser.add_argument('--top_p',type=float,default=1, |
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help='Provide generation top_p for LLMs.') |
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parser.add_argument("--gpt_azure", type=str2bool, nargs='?', |
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const=True, default=True, |
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help="Whether you are using the AzureOpenAI for GPT-models' response generation.") |
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args = parser.parse_args() |
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get_model_mc_response(model_name=args.model, |
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model_cache_dir=args.model_cache_dir, |
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mc_dir=args.mc_dir, |
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questions_file=args.questions_file, |
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response_file=args.response_file, |
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temperature=args.temperature, |
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top_p=args.top_p, |
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gpt_azure=args.gpt_azure) |