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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 os
import ast
import re
from datasets import load_dataset,get_dataset_config_names
HF_TOKEN=os.getenv("HF_TOKEN")
class MMLUTask(BaseTask):
def __init__(self, model_name):
self.subsets = get_dataset_config_names("metunlp/mmlu_tr")
print(self.subsets)
super().__init__("metunlp/mmlu_tr", model_name=model_name)
def load_dataset_from_hf(self):
evaluate_count = 50
dataset_dict = {}
for subset in self.subsets:
subset_data = load_dataset(self.dataset_repo, subset, token=HF_TOKEN, split="train")
dataset_dict[subset] = subset_data.select(range(min(evaluate_count, len(subset_data))))
return dataset_dict
def evaluate(self) -> dict[str, Any]:
responses = []
difficulty_results = defaultdict(lambda: {'correct': 0, 'total': 0})
total_count = 0
true = 0
for subset in self.subsets:
curr_dataset = self.dataset[subset]
print(curr_dataset[0])
for row in curr_dataset:
total_count += 1
# Get values from row
question = row["question"]
answer_index = row["answer"]
subject = row["subject"]
correct_answer_letter = chr(65 + answer_index)
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)])
# Construct the prompt/message
instruction = f"Aşağıda {subject} konusunda çoktan seçmeli bir soru verilmiştir."
prompt = f"{instruction}\n\nSoru: {question}\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[subset]['correct'] += 1
difficulty_results[subset]['total'] += 1
# Print results categorized by subset
for category, stats in difficulty_results.items():
correct = stats['correct']
total = stats['total']
calculatedAccuracy = correct / total if total > 0 else 0
print(f"{subset.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}
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