bstraehle commited on
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b2010da
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1 Parent(s): 809adb7

Update util.py

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  1. util.py +6 -6
util.py CHANGED
@@ -2,10 +2,9 @@ import pandas as pd
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  from openai import OpenAI
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- FILE_PATH = "data/gaia_validation_20.jsonl"
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-
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- def get_questions():
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- df = pd.read_json(FILE_PATH, lines=True)
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  result=[]
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  for index, row in df.iterrows():
@@ -13,7 +12,7 @@ def get_questions():
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  return result
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- def get_final_answer(model, question, initial_answer):
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  prompt_template = """
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  You are an expert question answering assistant. Given a question and an initial answer, your task is to provide the final answer.
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  Your final answer must be a number and/or string OR as few words as possible OR a comma-separated list of numbers and/or strings.
@@ -24,7 +23,7 @@ def get_final_answer(model, question, initial_answer):
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  **Question:** """ + question + """
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- **Initial answer:** """ + initial_answer + """
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  **Example 1:** How many 'r's are in strawberry? 3
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  **Example 2:** What is the opposite of black? White
@@ -37,6 +36,7 @@ def get_final_answer(model, question, initial_answer):
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  """
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  client = OpenAI()
 
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  completion = client.chat.completions.create(
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  messages=[{"role": "user", "content": [{"type": "text", "text": prompt_template}]}],
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  model=model
 
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  from openai import OpenAI
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+ def get_questions(file_path):
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+ df = pd.read_json(file_path, lines=True)
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+
 
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  result=[]
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  for index, row in df.iterrows():
 
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  return result
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+ def get_final_answer(model, question, answer):
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  prompt_template = """
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  You are an expert question answering assistant. Given a question and an initial answer, your task is to provide the final answer.
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  Your final answer must be a number and/or string OR as few words as possible OR a comma-separated list of numbers and/or strings.
 
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  **Question:** """ + question + """
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+ **Initial answer:** """ + answer + """
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  **Example 1:** How many 'r's are in strawberry? 3
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  **Example 2:** What is the opposite of black? White
 
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  """
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  client = OpenAI()
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+
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  completion = client.chat.completions.create(
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  messages=[{"role": "user", "content": [{"type": "text", "text": prompt_template}]}],
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  model=model