Duplicate from mshojaei77/konkur1404

.gitattributes

@@ -0,0 +1,59 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,277 @@
+---
+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.

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:683e2788cf05a945bb53d5bd1f3ced0e1dc8c4c46013f2fb95673d9c43527305
+size 10028467