| --- |
| license: mit |
| task_categories: |
| - question-answering |
| - multiple-choice |
| language: |
| - fa |
| - en |
| tags: |
| - konkur |
| - entrance-exam |
| - education |
| size_categories: |
| - 1K<n<10K |
| pretty_name: Konkur1404 (Persian MCQ) |
| dataset_name: konkur1404 |
| multimodal: true |
| llm_eval_ready: true |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: exam_name |
| dtype: string |
| - name: question |
| dtype: string |
| - name: choices |
| list: string |
| - name: answer_key |
| dtype: int32 |
| - name: figure |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 17853172 |
| num_examples: 2137 |
| download_size: 10028467 |
| dataset_size: 17853172 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| # Dataset Card for Konkur1404 |
|
|
| ## Dataset Description |
|
|
| This dataset contains questions from the Konkur (Iranian University Entrance Exam) for the year 1404. It is designed for evaluating models on Persian multiple-choice questions across various subjects. |
|
|
| ### Dataset Summary |
|
|
| - **Total Examples**: 2137 |
| - **Splits**: train |
| - **Languages**: Persian (fa) |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
|
|
| An example from the dataset looks like this: |
|
|
| ```json |
| { |
| "id": "ensani_nobat1_1", |
| "exam_name": "ensani_nobat1", |
| "question": "اگر شعاع دایره شکل زیر برابر $x = \\frac{1}{\\sqrt{2\\pi}}$ و مجموع مساحتهای دو شکل برابر ۱۶ باشد، محیط دایره کدام است؟", |
| "choices": [ |
| "$\\sqrt{\\pi}$", |
| "$2\\sqrt{\\pi}$", |
| "$3\\sqrt{\\pi}$", |
| "$4\\sqrt{\\pi}$" |
| ], |
| "answer_key": 4, |
| "figure": "<Image: PNG, (437, 231)>" |
| } |
| ``` |
|
|
| ### Data Fields |
|
|
| The dataset contains the following fields: |
|
|
| - **id** (string): Description of id. |
| - **exam_name** (string): Description of exam_name. |
| - **question** (string): Description of question. |
| - **choices** (List(Value('string'))): Description of choices. |
| - **answer_key** (int32): Description of answer_key. |
| - **figure** (PIL.Image.Image): Description of figure. |
|
|
| ## Dataset Statistics |
|
|
| ### Split: train |
| - Count: 2137 |
| - **exam_name Distribution**: |
| - zaban_nobat1: 400 |
| - zaban_nobat2: 350 |
| - ensani_nobat1: 280 |
| - tajrobi_nobat1: 225 |
| - ensani_nobat2: 221 |
| - tajrobi_nobat2: 185 |
| - riazi_nobat1: 145 |
| - honar_nobat1: 126 |
| - riazi_nobat2: 105 |
| - honar_nobat2: 100 |
| - **answer_key Distribution**: |
| - 1.0: 551 |
| - 2.0: 539 |
| - 3.0: 538 |
| - 4.0: 508 |
|
|
| ## Evaluation with OpenAI-Compatible API |
|
|
| - Deterministic settings (temperature=0) are recommended. |
| - Normalize Persian digits and English number words. |
| - Report both overall accuracy and per-exam accuracy. |
| - Use multimodal input for questions with figures if your model supports images. |
|
|
| ### Evaluation Script |
|
|
| ```python |
| import os |
| import io |
| import base64 |
| import re |
| import csv |
| import time |
| from collections import defaultdict |
| |
| from openai import OpenAI |
| from datasets import load_dataset |
| from tqdm import tqdm |
| |
| API_KEY = os.getenv("OPENROUTER_API_KEY") or os.getenv("OPENAI_API_KEY", "your-api-key") |
| BASE_URL = os.getenv("OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1") |
| MODEL_NAME = os.getenv("OPENROUTER_MODEL", "openai/gpt-5.2") |
| USE_IMAGES = True |
| EXAMS = ["ensani_nobat1", "ensani_nobat2"] |
| |
| client = OpenAI(api_key=API_KEY, base_url=BASE_URL) |
| |
| def format_prompt(example): |
| prompt = f"Question: {example['question']}\n\n" |
| for i, choice in enumerate(example['choices']): |
| prompt += f"{i+1}. {choice}\n" |
| prompt += "\nAnswer with the number of the correct choice (1, 2, 3, or 4) only." |
| return prompt |
| |
| def extract_answer(response_text): |
| text = response_text.strip() |
| for k, v in {"۱": "1", "۲": "2", "۳": "3", "۴": "4"}.items(): |
| text = text.replace(k, v) |
| for k, v in {"one": "1", "two": "2", "three": "3", "four": "4"}.items(): |
| if re.search(rf"\b{k}\b", text, flags=re.IGNORECASE): |
| text = v |
| break |
| for k, v in {"یک": "1", "يك": "1", "دو": "2", "سه": "3", "چهار": "4"}.items(): |
| if k in text: |
| text = v |
| break |
| m = re.search(r"\b([1-4])\b", text) |
| return int(m.group(1)) if m else None |
| |
| def figure_to_base64(figure): |
| if not figure: |
| return None |
| try: |
| if hasattr(figure, "save"): |
| buf = io.BytesIO() |
| figure.save(buf, format="PNG") |
| return base64.b64encode(buf.getvalue()).decode("utf-8") |
| if isinstance(figure, str): |
| path = figure |
| if not os.path.isabs(path): |
| path = os.path.join(os.getcwd(), path) |
| from PIL import Image |
| img = Image.open(path) |
| buf = io.BytesIO() |
| img.save(buf, format="PNG") |
| return base64.b64encode(buf.getvalue()).decode("utf-8") |
| except Exception: |
| return None |
| return None |
| |
| def chat_with_retries(messages, max_retries=3): |
| delay = 1.0 |
| for attempt in range(max_retries): |
| try: |
| return client.chat.completions.create( |
| model=MODEL_NAME, |
| messages=messages, |
| temperature=0, |
| max_tokens=10 |
| ) |
| except Exception: |
| if attempt == max_retries - 1: |
| raise |
| time.sleep(delay) |
| delay = min(8.0, delay * 2) |
| |
| def evaluate(): |
| ds = load_dataset("mshojaei77/konkur1404", split="train") |
| if EXAMS: |
| ds = ds.filter(lambda x: x.get("exam_name") in EXAMS) |
| |
| totals = defaultdict(int) |
| corrects = defaultdict(int) |
| rows = [] |
| |
| for example in tqdm(ds): |
| |
| prompt = format_prompt(example) |
| messages = [{"role": "system", "content": "Answer only with 1, 2, 3, or 4."}] |
| |
| img_b64 = None |
| if USE_IMAGES: |
| img_b64 = figure_to_base64(example.get("figure")) |
| |
| if USE_IMAGES and img_b64: |
| messages.append({ |
| "role": "user", |
| "content": [ |
| {"type": "text", "text": prompt}, |
| {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_b64}"}} |
| ] |
| }) |
| else: |
| messages.append({"role": "user", "content": prompt}) |
| |
| pred = None |
| error_msg = "" |
| try: |
| resp = chat_with_retries(messages) |
| prediction_text = resp.choices[0].message.content.strip() |
| pred = extract_answer(prediction_text) |
| except Exception as e: |
| error_msg = str(e) |
| |
| gt = int(example["answer_key"]) |
| exam = example.get("exam_name", "unknown") |
| totals[exam] += 1 |
| ok = int(pred == gt) |
| corrects[exam] += ok |
| rows.append({"id": example.get("id"), "exam_name": exam, "predicted": pred, "ground_truth": gt, "correct": ok, "error": error_msg}) |
| if error_msg: |
| print(f"Error on id={example.get('id')} exam={exam}: {error_msg}") |
| |
| total = sum(totals.values()) |
| correct = sum(corrects.values()) |
| if total: |
| print(f"Accuracy: {100*correct/total:.2f}% ({correct}/{total})") |
| for exam, t in totals.items(): |
| if t: |
| print(f"- {exam}: {100*corrects[exam]/t:.2f}% ({corrects[exam]}/{t})") |
| else: |
| print("No examples evaluated.") |
| |
| if rows: |
| with open("konkur1404_results.csv", "w", newline="", encoding="utf-8") as f: |
| w = csv.DictWriter(f, fieldnames=["id","exam_name","predicted","ground_truth","correct","error"]) |
| w.writeheader() |
| w.writerows(rows) |
| print("Saved konkur1404_results.csv") |
| |
| if __name__ == "__main__": |
| evaluate() |
| ``` |
| ### Data Notes |
|
|
| - Choices are always 4 options; answer_key is 1–4 (1-based). |
| - Figures are PNGs referenced by relative paths; when loaded via HF Datasets, figure may be an image object. |
| - Text may include LaTeX-style math and Persian digits; normalize for robust parsing. |
| |
| ### Ethics and Usage |
| |
| - For evaluation and research use; respect exam policies and local regulations. |
| - Random baseline is 25% accuracy; report per-exam breakdown for interpretability. |
| |
