Spaces:
Paused
Paused
File size: 2,906 Bytes
ca54ffd f6890a5 ca54ffd 9828c0e ca54ffd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
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}
|