Spaces:
Paused
Paused
File size: 3,429 Bytes
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 72 73 74 75 76 77 78 |
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_split_names
HF_TOKEN=os.getenv("HF_TOKEN")
class MetaphorsAndIdiomsTask(BaseTask):
def __init__(self, model_name):
super().__init__("metunlp/metaphors_and_idioms", model_name=model_name)
def load_dataset_from_hf(self):
dataset = super().load_dataset_from_hf()
return dataset # dataset.select(range(min(10, len(dataset))))
def evaluate(self) -> dict[str, Any]:
responses = []
difficulty_results = defaultdict(lambda: defaultdict(lambda: {'correct': 0, 'total': 0}))
total_count = 0
true = 0
for row in self.dataset:
total_count += 1
# Get values from row
category = "hard" if row["level"]== 1 else "easy" if row["level"] == 0 else None
answer_index = row["answer"]
correct_answer_letter = chr(65 + answer_index)
context = row["context"]
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)])
subset = row["idiom_type"]
if subset == "atasözü":
question = "Aşağıda verilen durum hangi atasözü ile en iyi ifade edilebilir?"
elif subset == "deyim":
question = """Verilen bağlamda "[MASKED]" ile boş bırakılan yere hangi deyim getirilirse cümlenin akışı anlamlı olur?"""
else:
question = "Aşağıda verilen durum hangi atasözü ile en iyi ifade edilebilir?"
# Construct the prompt/message
instruction = ""
prompt = f"Soru: {question}\nBağlam: {context}\nSeçenekler:\n{formatted_choices}\n{instruction}\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][category]['correct'] += 1
difficulty_results[subset][category]['total'] += 1
# Print results categorized by difficulty
for subset in difficulty_results.keys():
subset_results = difficulty_results[subset]
for category, stats in subset_results.items():
correct = stats['correct']
total = stats['total']
calculatedAccuracy = correct / total if total > 0 else 0
print(f"{subset.capitalize()} {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}
|