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def generate_prompt(source_passage: str) -> str:
"""
Generates a prompt for a chatbot to summarize a given passage.
Args:
source_passage (str): The passage to be summarized.
Returns:
str: A formatted prompt string for the chatbot.
"""
if not source_passage:
raise ValueError("Source passage is empty.")
return f"""You are a chat bot answering questions using data. You must stick to the answers provided solely by the text in the passage provided.
You are asked the question 'Provide a concise summary of the following passage, covering the core pieces of information described:'
Passage:\n {source_passage}
"""
def format_results(model_name: str, revision: str, precision: str, accuracy: float,
hallucination_rate: float, answer_rate: float, avg_summary_len: float,
error_rate: float) -> dict:
"""
Formats the evaluation results into a structured dictionary.
Args:
model_name (str): The name of the evaluated model.
revision (str): The revision hash of the model.
precision (str): The precision with which the evaluation was run.
accuracy (float): The accuracy score from the evaluation.
hallucination_rate (float): The hallucination rate from the evaluation.
answer_rate (float): The answer rate from the evaluation.
avg_summary_len (float): The average summary length from the evaluation.
error_rate (float): The rate at which errors occurred during summary generation.
Returns:
dict: A dictionary containing the structured evaluation results.
"""
results = {
"config": {
"model_dtype": precision, # Precision with which you ran the evaluation
"model_name": model_name, # Name of the model
"model_sha": revision # Hash of the model
},
"results": {
"accuracy": {
"accuracy": accuracy
},
"hallucination_rate": {
"hallucination_rate": hallucination_rate
},
"answer_rate": {
"answer_rate": answer_rate
},
"average_summary_length": {
"average_summary_length": avg_summary_len
},
"error_rate": {
"error_rate": error_rate
}
}
}
return results
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