streamlit / hallucinator.py
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class HallucinatonEvaluater:
def __init__(self, item):
self.question = item["question"]
self.answer = item["answer"]
#self.domain = item["domain"]
self.context = item["context"]
self.llm=HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":1, "max_length":1000000})
def get_prompt_template(self):
prompt = HallucinatePromptContext()
template = prompt.base_template
varialbles = prompt.variables_list
eval_template = PromptTemplate(input_variables=varialbles, template=template)
return eval_template
def evaluate(self):
prompt = self.get_prompt_template().format(query = self.question, answer = self.answer, context = self.context)
score = self.llm(prompt)
return score