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