t-tech/ruMMLU-pro

---
license: odc-by
---
# ruMMLU-Pro

## 📝 Dataset Summary
**ruMMLU-Pro** is a high-quality Russian translation of the MMLU-Pro dataset, containing **11,986** examples. The dataset preserves the full structure, labeling scheme, answer indices, and chain-of-thought content from the original benchmark while providing localized Russian text suitable for evaluating multilingual and Russian-native language models.
**ruMMLU-Pro** enables rigorous assessment of Russian-native and multilingual language models on high-level reasoning tasks, offering strong coverage of domains such as medicine, law, business, STEM disciplines, humanities, and more.

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## 📁 Dataset Structure

### **Data Fields**

| Field | Type | Description |
|----------------|------------|-------------|
| `question_id` | `int` | Unique question identifier |
| `question` | `string` | Russian-translated question prompt |
| `options` | `list[str]`| List of answer options |
| `answer` | `string` | Correct answer label (typically A/B/C/D or similar) |
| `answer_index` | `int` | Index of the correct option |
| `cot_content` | `string` | Chain-of-thought explanation supporting the answer |
| `category` | `string` | Domain category label |
| `src` | `string` | Provenance reference to original English task |

### **Example Entry**

```json
{"question_id": 262,
"question": "Как называется скорость, с которой рынок принимает инновацию?",
"options": ["Скорость проникновения на рынок.", "Процесс принятия.",
"Шкала принятия рынком.", "Процесс насыщения рынка.",
"Процесс дифференциации.", "Жизненный цикл продукта.",
"Процесс диффузии.", "Скорость инноваций.",
"Процесс диверсификации.", "Жизненный цикл принятия."],
"answer": "G",
"answer_index": 6,
"cot_content": "",
"category": "business",
"src": "ori_mmlu-marketing"
}
```