---
license: cc-by-4.0
language:
- ru
pretty_name: Russian NMO Medical MCQ
task_categories:
- question-answering
- text-generation
tags:
- russian
- medical
- nmo
- multiple-choice
- supervised-fine-tuning
configs:
- config_name: mcq
  data_files:
  - split: train
    path: data/mcq/train-*.parquet
  - split: validation
    path: data/mcq/validation-*.parquet
  - split: test
    path: data/mcq/test-*.parquet
- config_name: sft_single
  data_files:
  - split: train
    path: data/sft_single/train-*.parquet
  - split: validation
    path: data/sft_single/validation-*.parquet
  - split: test
    path: data/sft_single/test-*.parquet
- config_name: sft_multi
  data_files:
  - split: train
    path: data/sft_multi/train-*.parquet
  - split: validation
    path: data/sft_multi/validation-*.parquet
  - split: test
    path: data/sft_multi/test-*.parquet
- config_name: sft_all
  data_files:
  - split: train
    path: data/sft_all/train-*.parquet
  - split: validation
    path: data/sft_all/validation-*.parquet
  - split: test
    path: data/sft_all/test-*.parquet
---

# Russian NMO Medical MCQ

Russian-language NMO medical test questions with single-correct and multiple-correct
multiple-choice formats, prepared for LLM fine-tuning and evaluation.

This dataset is intended for educational test-answering, supervised fine-tuning,
retrieval/evaluation experiments, and benchmark construction. It is not clinical
decision support.

## Configs

- `mcq`: canonical structured examples.
- `sft_single`: chat/instruction rows for single-correct-choice questions.
- `sft_multi`: chat/instruction rows for multiple-correct-choice questions.
- `sft_all`: concatenation of `sft_single` and `sft_multi`.

## Loading

```python
from datasets import load_dataset

mcq = load_dataset("drkolesnikov/russian-nmo-medical-mcq", "mcq")
sft_all = load_dataset("drkolesnikov/russian-nmo-medical-mcq", "sft_all")
```

For a local export:

```python
from datasets import load_dataset

mcq = load_dataset(".", "mcq")
sft_all = load_dataset(".", "sft_all")
```

## Schema

`mcq` columns:

```text
id
topic
question
choices
correct_choice_indices
correct_choice_texts
choice_count
correct_choice_count
is_multi_answer
task_type
```

SFT columns:

```text
id
topic
task_type
correct_choice_indices
messages
prompt
completion
```

`task_type` is one of:

- `single_choice`
- `multiple_correct`

Answer indices are zero-based and preserve the original answer-choice order.

## Prompt Format

SFT prompts include the topic when available, then the question and zero-based
answer choices. SFT completions contain only selected answer number(s) and answer
text, with no explanations.

Single-correct example:

```text
Ответ: 2. системный воспалительный ответ
```

Multiple-correct example:

```text
Ответы: 0, 2, 4. лихорадка; тахикардия; лейкоцитоз
```

## Splits

Splits are deterministic and grouped by normalized question ID:

- train: 90%
- validation: 5%
- test: 5%

All variants derived from the same normalized question stay in the same split.

## Counts

| Config | Train | Validation | Test | Total |
| --- | ---: | ---: | ---: | ---: |
| `mcq` | 384,286 | 21,332 | 21,342 | 426,960 |
| `sft_single` | 260,561 | 14,546 | 14,640 | 289,747 |
| `sft_multi` | 123,725 | 6,786 | 6,702 | 137,213 |
| `sft_all` | 384,286 | 21,332 | 21,342 | 426,960 |

Excluded from all public configs:

- conflicting answer variants: 449
- answer sets attached to conflicting variants: 914
- answer sets with markdown/source-page artifacts: 4

## License

Released under `cc-by-4.0`.