README.md

---
dataset_info:
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: category
    dtype: string
  - name: module
    dtype: string
  splits:
  - name: ru
    num_bytes: 2068198005
    num_examples: 12699944
  - name: en
    num_bytes: 13818553700
    num_examples: 112709888
  download_size: 4606972272
  dataset_size: 15886751705
configs:
- config_name: default
  data_files:
  - split: ru
    path: data/ru-*
  - split: en
    path: data/en-*
license: apache-2.0
language:
  - en
  - ru
task_categories:
  - text-generation
  - question-answering
tags:
  - mathematics
  - synthetic
  - reasoning
---

# Mathematical Reasoning Dataset (English & Russian)

## Dataset Description

- **Repository:** <https://github.com/google-deepmind/mathematics_dataset>
- **Paper:** [Analysing Mathematical Reasoning Abilities of Neural Models](https://identifiers.org/arxiv:1904.01557)

A bilingual collection of synthetic school-level mathematics questions and answers, based on the DeepMind [mathematics_dataset](https://github.com/google-deepmind/mathematics_dataset) generator.

This dataset contains two language splits:

- `en` — the **original English data**, taken as-is from the official `mathematics_dataset-v1.0` release published by Google DeepMind ([github.com/google-deepmind/mathematics_dataset](https://github.com/google-deepmind/mathematics_dataset)).
- `ru` — a **Russian version generated from scratch** with a translated fork of the generator. It is **not a translation** of the English rows: all question templates were rewritten in Russian (with corrected grammar and noun/adjective declension), and the problems were sampled independently by the generator. Row-level pairing with the English data is not guaranteed.

Each split is a single table with a `category` column (difficulty / evaluation split) and a `module` column (mathematical topic).

## Dataset structure

```python
from datasets import load_dataset

ds = load_dataset("your-username/math-dataset-bilingual")

# ds["en"] and ds["ru"] are Dataset objects with the same columns:
#   question : str   — the math problem
#   answer   : str   — the expected answer
#   category : str   — train-easy | train-medium | train-hard | interpolate | extrapolate
#   module   : str   — e.g. algebra__linear_1d, arithmetic__add_or_sub, measurement__conversion
#   language : str   — en | ru
```

### Row counts

| Split | Rows |
|-------|------|
| `en` | 112,709,888 |
| `ru` | 12,699,944 |

The English split is the full official release (~2M generated examples per train module); the Russian split was generated with `--per_train_module=1000000 --per_test_module=1000000 --seed=42`, hence roughly 10× smaller.

### Example row (English)

```json
{
  "question": "Let 2*a**5 + 15648*a**4 - 5632498*a**3 + 172753368*a**2 + 189729080*a - 356865600 = 0. Calculate a.",
  "answer": "-8170, -2, 1, 35, 312",
  "category": "extrapolate",
  "module": "algebra__polynomial_roots_big",
  "language": "en"
}
```

### Example row (Russian)

```json
{
  "question": "Пусть 2*a**5 + 15648*a**4 - 5632498*a**3 + 172753368*a**2 + 189729080*a - 356865600 = 0. Чему равно a?",
  "answer": "-8170, -2, 1, 35, 312",
  "category": "extrapolate",
  "module": "algebra__polynomial_roots_big",
  "language": "ru"
}
```

## Splits and categories

| Category | Content |
|----------|---------|
| `train-easy`   | Easier training examples |
| `train-medium` | Medium-difficulty training examples |
| `train-hard`   | Harder training examples |
| `interpolate`  | In-distribution test examples |
| `extrapolate`  | Out-of-distribution / extrapolation test examples |

The dataset covers 70 task modules: algebra, arithmetic, calculus, comparison, conversion, divisibility, gcd/lcm, geometry, measurement, numbers, polynomials, probability.

You can filter by category or module directly with the `datasets` library:

```python
# Russian training examples only
ru_train = ds["ru"].filter(lambda row: row["category"].startswith("train"))

# English algebra only
en_algebra = ds["en"].filter(lambda row: row["module"].startswith("algebra__"))

# A specific module across both languages
from datasets import concatenate_datasets

measurement = concatenate_datasets([
    ds["ru"].filter(lambda row: row["module"] == "measurement__conversion"),
    ds["en"].filter(lambda row: row["module"] == "measurement__conversion"),
])
```

## How the dataset was built

1. **English data** — extracted unmodified from the official release archive `mathematics_dataset-v1.0.tar.gz` distributed with the [google-deepmind/mathematics_dataset](https://github.com/google-deepmind/mathematics_dataset) repository.
2. **Russian data** — generated from scratch with a fork of the same repository in which every question template, unit name and textual fragment was rewritten in Russian (including grammatically correct declensions, e.g. in unit conversions). The generator was then run with `--per_train_module=1000000 --per_test_module=1000000 --seed=42` to sample an entirely new Russian dataset. No machine translation of the English rows was used.
3. **Conversion** — raw text files (alternating question/answer lines, one file per `{category}/{module}`) were parsed, paired, and annotated with `category`, `module` and `language` columns.

## Citation

If you use this dataset, please cite the original paper:

```bibtex
@inproceedings{saxton2019analysing,
  title={Analysing Mathematical Reasoning Abilities of Neural Models},
  author={Saxton, David and Grefenstette, Edward and Hill, Felix and Kohli, Pushmeet},
  booktitle={International Conference on Learning Representations},
  year={2019}
}
```

## License

Apache License 2.0 (same as the original DeepMind repository).