| from utils import * |
|
|
| parser = argparse.ArgumentParser(description='Choose your model(s) & language(s)') |
| parser.add_argument('--model',type=str, |
| 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").') |
| parser.add_argument('--language',type=str,default=None, |
| help='Provide the language you want to test on. Check and choose from the first values of the LANG_COUNTRY variable. If you want to test on multiple languages, provide multiple languages with ", " between each (e.g., "English, Korean").') |
| parser.add_argument('--country',type=str,default=None, |
| help='Provide the country you want to test on. Check and choose from the second values of the LANG_COUNTRY variable. If you want to test on multiple countries, provide multiple countries with ", " between each (e.g., "UK, South Korea"). Make sure you have the same number of countries and languages provided. The language-country pair do not have to be identical with the pairs within the LANG_COUNTRY variable.') |
| parser.add_argument('--question_dir',type=str,default=None, |
| help='Provide the directory name with (translated) questions.') |
| parser.add_argument('--question_file',type=str,default=None, |
| help='Provide the csv file name with (translated) questions.') |
| parser.add_argument('--question_col',type=str,default=None, |
| help='Provide the column name from the given csv file name with (translated) questions.') |
| parser.add_argument('--prompt_dir',type=str,default=None, |
| help='Provide the directory where the propmts are saved.') |
| parser.add_argument('--prompt_file',type=str,default=None, |
| help='Provide the name of the csv file where the propmts are saved.') |
| parser.add_argument('--prompt_no',type=str,default=None, |
| help='Provide the propmt id (ex. inst-1, inst-2, pers-1, etc.)') |
| parser.add_argument('--id_col',type=str,default="ID", |
| help='Provide the column name from the given csv file name with question IDs.') |
| parser.add_argument('--output_dir',type=str,default='./model_inference_results', |
| help='Provide the directory for the output files to be saved.') |
| parser.add.argument('--output_file',type=str,default=None, |
| help='Provide the name of the output file.') |
| parser.add_argument('--model_cache_dir',type=str,default='.cache', |
| help='Provide the directory saving model caches.') |
| parser.add_argument("--gpt_azure", type=str2bool, nargs='?', |
| const=True, default=False, |
| help="Whether you are using the AzureOpenAI for GPT-models' response generation.") |
| parser.add_argument('--temperature',type=int,default=0, |
| help='Provide generation temperature for GPT models.') |
| parser.add_argument('--top_p',type=int,default=0, |
| help='Provide generation top_p for GPT models.') |
|
|
| args = parser.parse_args() |
|
|
| def make_prompt(question,prompt_no,language,country,prompt_sheet): |
| prompt = prompt_sheet[prompt_sheet['id']==prompt_no] |
| if language == 'English': |
| prompt = prompt['English'].values[0] |
| else: |
| prompt = prompt['Translation'].values[0] |
|
|
| return prompt.replace('{q}',question) |
|
|
| def generate_response(model_name,model_path,tokenizer,model,language,country,q_df,q_col,id_col,output_dir,prompt_no=None): |
| replace_country_flag = False |
| if language != COUNTRY_LANG[country] and language == 'English': |
| replace_country_flag = True |
| |
| if q_col == None: |
| if language == COUNTRY_LANG[country]: |
| q_col = 'Translation' |
| elif language == 'English': |
| q_col = 'Question' |
| |
| if prompt_no is not None: |
| prompt_sheet = import_google_sheet(PROMPT_SHEET_ID,PROMPT_COUNTRY_SHEET[country]) |
| output_filename = os.path.join(output_dir,f"{model_name}-{country}_{language}_{prompt_no}_result.csv") |
| else: |
| output_filename = os.path.join(output_dir,f"{model_name}-{country}_{language}_result.csv") |
| print(q_df[[id_col,q_col]]) |
| |
| guid_list = set() |
| if os.path.exists(output_filename): |
| already = pd.read_csv(output_filename) |
| guid_list = set(already[id_col]) |
| print(already) |
| |
| |
| else: |
| write_csv_row([id_col,q_col,'prompt','response','prompt_no'],output_filename) |
| |
| pb = tqdm(q_df.iterrows(),desc=model_name,total=len(q_df)) |
| for _,d in pb: |
| q = d[q_col] |
| guid = d[id_col] |
| pb.set_postfix({'ID':guid}) |
| |
| if guid in guid_list: |
| continue |
| |
| if replace_country_flag: |
| q = replace_country_name(q,country.replace('_',' ')) |
| |
| if prompt_no is not None: |
| prompt = make_prompt(q,prompt_no,language,country,prompt_sheet) |
| else: |
| prompt = q |
| |
| print(prompt) |
| |
| response = get_model_response(model_path,prompt,model,tokenizer,temperature=args.temperature,top_p=args.top_p,gpt_azure=args.gpt_azure) |
| |
| print(response) |
| write_csv_row([guid,q,prompt,response,prompt_no],output_filename) |
| |
| del guid_list |
| |
| def get_response_from_all(): |
| models = args.model |
| languages = args.language |
| countries = args.country |
| question_dir = args.question_dir |
| question_file = args.question_file |
| question_col = args.question_col |
| prompt_no = args.prompt_no |
| id_col = args.id_col |
| output_dir = args.output_dir |
| azure = args.gpt_azure |
| |
| if not os.path.exists(output_dir): |
| os.mkdir(output_dir) |
| |
| if args.gpus: |
| os.environ['CUDA_VISIBLE_DEVICES'] = args.gpus |
| |
| if ',' in languages: |
| languages = languages.split(',') |
| |
| if ',' in countries: |
| countries = countries.split(',') |
| |
| if ', ' in models: |
| models = models.split(',') |
| |
| if type(languages) == type(countries) and isinstance(languages,list): |
| if len(languages) != len(countries): |
| print("ERROR: Same number of languages and countries necessary. If multiple languages and countries are given, each element of the two lists should be in pairs.") |
| exit() |
| |
| def get_questions(language,country): |
| questions_df = pd.read_csv(os.path.join(question_dir,f'{country}_full_final_questions.csv'),encoding='utf-8') |
|
|
| return questions_df |
| |
| |
| def generate_response_per_model(model_name): |
| model_path = MODEL_PATHS[model_name] |
| |
| tokenizer,model = get_tokenizer_model(model_name,model_path,args.model_cache_dir) |
| |
| if isinstance(languages,str): |
| |
| questions = get_questions(languages,countries) |
| generate_response(model_name,model_path,tokenizer,model,languages,countries,questions,question_col,id_col,output_dir,prompt_no=prompt_no) |
| else: |
| for l,c in zip(languages,countries): |
| questions = get_questions(l,c) |
| generate_response(model_name,model_path,tokenizer,model,l,c,questions,question_col,id_col,output_dir,prompt_no=prompt_no) |
| |
| if isinstance(models,str): |
| generate_response_per_model(models) |
| else: |
| for m in models: |
| generate_response_per_model(m) |
|
|
| |
| if __name__ == "__main__": |
| get_response_from_all() |
