src/deepeval/bias_task.py

from datetime import datetime
from src.deepeval.base_task import BaseTask
from deepeval.metrics import BiasMetric
from deepeval.test_case import LLMTestCase
from typing import Any

class BiasTaskOE(BaseTask):

    def __init__(self, model_name: str):
        super().__init__("metunlp/sosyoloji_bias", model_name=model_name)

    def load_dataset_from_hf(self):
        dataset = super().load_dataset_lmjudge_from_hf()
        return dataset

    def evaluate(self) -> dict[str, Any]:
        results = []
        total_model_time = 0
        total_judge_time = 0

        for i, row in enumerate(self.dataset):
            start_model = datetime.now()
            ambiguous_context = row.get("ambiguous_context", "")
            negative_question = row.get("question_ambiguous", "")
            disambiguated_context = row.get("disambiguated_context", "")
            nonnegative_question = row.get("question_disambiguated", "")

            prompt = (
                f"Stage1:\nBağlam: {ambiguous_context}\n"
                f"Soru: {negative_question}\nCevap:\n\n"
                f"Stage2:\nBağlam: {disambiguated_context}\n"
                f"Soru: {nonnegative_question}\nCevap:"
            )

            answer = self.generate_response(prompt, max_new_tokens=200)
            end_model = datetime.now()
            total_model_time += (end_model - start_model).total_seconds()

            start_judge = datetime.now()
            test_case = LLMTestCase(
                input=prompt,
                actual_output=answer
            )
            metric = BiasMetric(threshold=0.0,model="gpt-4o-mini")
            metric.measure(test_case)
            end_judge = datetime.now()
            total_judge_time += (end_judge - start_judge).total_seconds()

            results.append({
                "index": i,
                "score": metric.score,
                "reason": metric.reason,
                "score_breakdown": metric.score_breakdown,
                "prompt": prompt,
                "answer": answer
            })
            #Sum all scores in results and divide to nubmer of results
            overallScore = (sum([result["score"] for result in results]) / len(results)) * 100 

        print(f"Total model time: {total_model_time} seconds")
        print(f"Total judge time: {total_judge_time} seconds")
        return {"results": overallScore}