src/deepeval/topic_detection.py

from src.deepeval.base_task import BaseTask
from collections import defaultdict
from src.deepeval.utils import accuracy, accuracy_standard_error
from typing import Any
import ast


class TopicDetectionTask(BaseTask):
    def __init__(self, model_name):
        super().__init__("metunlp/topic_detection_tr", model_name=model_name)

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


    def evaluate(self) -> dict[str, Any]:
        responses = []
        difficulty_results = defaultdict(lambda: {'correct': 0, 'total': 0})
        total_count = 0
        true = 0

        for row in self.dataset:
            total_count += 1

            # Get values from row
            choices = ast.literal_eval(row["choices"]) # Convert string to list
            formatted_choices = "\n".join([f"{chr(65+i)}: {choice}" for i, choice in enumerate(choices)])
            category = row["level"].lower().replace(' ','')
            answer = row["answer"]
            text = row["text"]

            # Get answer index (starting from 0)
            if type(answer) == int:
                answer_index = answer
            else:
                answer_index = int(answer)
            correct_answer_letter = chr(65 + answer_index)


            # Construct the prompt/message
            instruction = "Aşağıdaki metni analiz et ve seçeneklerden bu metnin en olası kategorisini belirle. Temaya ve detaylara dikkat ederek metnin ana fikrini göz önünde bulundurarak soruyu cevapla."
            prompt = f"{instruction}\n\nMetin:\n{text}\nSeçenekler:\n{formatted_choices}\n\n"
            message = prompt

            # Get/format answer of the model
            model_answer = self.generate_response_mcqa_multi_token(message, choices=choices, max_new_tokens=2)
            responses.append(model_answer)
            model_answer_cleaned = model_answer.strip().replace('\n', '').replace(' ', '').upper().replace(':','')

            # Check if correct based on metric
            if correct_answer_letter == model_answer_cleaned:
                true += 1
                difficulty_results[category]['correct'] += 1

            difficulty_results[category]['total'] += 1

        # Print results categorized by difficulty
        for category, stats in difficulty_results.items():
            correct = stats['correct']
            total = stats['total']
            calculatedAccuracy = correct / total if total > 0 else 0
            print(f"{category.capitalize()} Accuracy: {calculatedAccuracy:.2%} ({correct}/{total})")

        print("Results:", responses)
        print("Overall Accuracy:", true / total_count)
        acc = accuracy(true, total_count)
        acc_stderr = accuracy_standard_error(acc, total_count)
        return {"acc": acc, "acc_stderr": acc_stderr}