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Download evaluation/evaluation_utils.py from uilab/BLEnD: direct link, hf CLI and curl.
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https://huggingface.co/datasets/uilab/BLEnD/resolve/1085fcb8e5349e876e2a62d8f4ed25028f86f685/evaluation/evaluation_utils.py
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hf download hf://datasets/uilab/BLEnD@1085fcb8e5349e876e2a62d8f4ed25028f86f685/evaluation/evaluation_utils.py
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curl -L -o evaluation_utils.py https://huggingface.co/datasets/uilab/BLEnD/resolve/1085fcb8e5349e876e2a62d8f4ed25028f86f685/evaluation/evaluation_utils.py
4.47 kB
| import sys | |
| import os | |
| parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) | |
| sys.path.append(parent_dir) | |
| from utils import * | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| COUNTRY_ISO = { | |
| "UK": "GB", | |
| "US": "US", | |
| "South_Korea": "KR", | |
| "Algeria": "DZ", | |
| "China": "CN", | |
| "Indonesia": "ID", | |
| "Spain": "ES", | |
| "Iran": "IR", | |
| "Mexico":"MX", | |
| "Assam":"AS", | |
| "Greece":"GR", | |
| "Ethiopia":"ET", | |
| "Northern_Nigeria":"NG", | |
| "Azerbaijan":"AZ", | |
| "North_Korea":"KP", | |
| "West_Java":"JB" | |
| } | |
| LANG_CODE = { | |
| 'English':'en', | |
| 'Chinese':'zh', | |
| 'Spanish':'es', | |
| 'Indonesian':'id', | |
| 'Greek':'el', | |
| 'Sundanese':'su', | |
| 'Azerbaijani':'az', | |
| 'Korean':'ko', | |
| 'Arabic':'ar', | |
| 'Persian':'fa', | |
| 'Assamese':'as', | |
| 'Amharic':'am', | |
| 'Hausa':'ha', | |
| } | |
| def get_questions( | |
| filename=None, | |
| data_dir=None, | |
| country=None, | |
| template='{country}_final_questions.csv' | |
| ): | |
| if filename == None: | |
| filename = template.replace('{country}',country.replace(' ','_')) | |
| if data_dir == None: | |
| assert 'ERROR: No data directory given' | |
| df = pd.read_csv(os.path.join(data_dir,filename),encoding='utf-8') | |
| return df | |
| def get_annotations( | |
| filename=None, | |
| data_dir=None, | |
| country=None, | |
| template='{country}_data_aggregated.json' | |
| ): | |
| if filename == None: | |
| filename = template.replace('{country}',country.replace(' ','_')) | |
| if data_dir == None: | |
| assert 'ERROR: No data directory given' | |
| with open(os.path.join(data_dir,filename),'r') as f: | |
| country_data = json.load(f) | |
| return country_data | |
| def get_model_response_file( | |
| filename=None, | |
| data_dir=None, | |
| model=None, | |
| country=None, | |
| language=None, | |
| prompt_no=None, | |
| template='{model}-{country}_{language}_{prompt_no}_result.csv' | |
| ): | |
| if filename == None: | |
| filename = template.replace('{model}',model).replace('{country}',country.replace(' ','_')).replace('{language}',language).replace('{prompt_no}',prompt_no) | |
| print(filename) | |
| if data_dir == None: | |
| assert 'ERROR: No data directory given' | |
| model_res_df = pd.read_csv(os.path.join(data_dir,filename),encoding='utf-8') | |
| return model_res_df | |
| def delete_prompt_from_answer(text,prompt): | |
| """ | |
| The function `delete_prompt_from_answer` aims to remove 'Answer:' part from the LLM response if there is any. | |
| :param text: LLM response | |
| :return: LLM response with 'Answer:' part removed | |
| """ | |
| # Regular expression to find a word followed by a colon, capturing the word before the last colon | |
| text = text.replace(prompt,'').replace(':',':').replace('、',',').replace(',',',').replace('。','.').lower() | |
| prompt = prompt.replace(':',':').replace('、',',').replace(',',',').replace('。','.').lower() | |
| match = re.findall(r'^(\w+:)\s', text) | |
| extracted = '' | |
| for m in match: | |
| if len(m) > len(extracted) and m.replace(':','') in prompt: | |
| extracted = m | |
| if match: | |
| return text.replace(extracted,'').strip() # Return the captured word | |
| else: | |
| return text.strip() # Return an empty string if no pattern is found | |
| def get_llm_response_by_id(res_df,qid,id_col,r_col): | |
| if qid not in set(res_df[id_col]): | |
| print(qid,'not in LLM response df') | |
| return None | |
| try: | |
| llm_response = res_df[res_df[id_col]==qid][r_col].values[-1] | |
| prompt = res_df[res_df[id_col]==qid]['prompt'].values[-1] | |
| llm_response = delete_prompt_from_answer(llm_response,prompt) | |
| llm_response = llm_response.strip('.').lower() | |
| except: | |
| print(res_df[res_df[id_col]==qid]) | |
| llm_response = None | |
| return llm_response | |
| def get_nested_json_str(response): | |
| """Extract json object from LLM response | |
| Args: | |
| response (str): LLM response with JSON format included | |
| Returns: | |
| dict: Extracted json (dict) object | |
| """ | |
| try: | |
| response = response.replace('\n','') | |
| if "{" not in response: | |
| print(response) | |
| return response | |
| response = response.replace('```json','').replace('`','').replace(',}','}') | |
| jsons = re.findall(r'{.+}',response) | |
| response = jsons[-1] | |
| json_object = json.loads(response) | |
| except: | |
| return response | |
| return json_object | |