| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - math |
| - reasoning |
| - mathematics |
| - Cot |
| - ToT |
| - TrainOfThought |
| pretty_name: Train Of Thought - Mathematics Version 1.0.0 |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: ToT-Math-26k.json |
| --- |
| |
|  |
|
|
| # Train of Thought: Mathematics V1 |
|
|
| Synthetic English question–answer pairs with written reasoning for mathematical problem solving, from elementary topics to advanced mathematics. |
|
|
| ## Contents |
|
|
| **Format:** English text; JSON records with `question`, `answer`, `id`, and `metadata` (difficulty, topic, and generated reasoning). |
|
|
| **Topics:** Arithmetic, geometry, algebra, real analysis, abstract algebra, mathematical physics, and applications in fields such as data science and finance. |
|
|
| [Full curriculum](https://mattwesney.substack.com/p/tot-math-technical-curriculum). |
|
|
| ## Creation and limitations |
|
|
| The Genesis II pipeline generated the dataset using Qwen, Phi, and Llama models with structured prompting and format checks. The written reasoning was designed to mimic o1-style explanations. |
|
|
| Synthetic proofs, calculations, and explanations may be incorrect or incomplete. Independently verify results before using them for evaluation, instruction, or consequential work. |
|
|
| ## Use |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("mattwesney/ToT-Math-V1") |
| ``` |
|
|
| Authenticate with an account that has access to this private dataset. |
|
|
| **License:** MIT. **Version:** 1.0.0. |
|
|
| ## Citation |
|
|
| Matthew Wesney (2025), *Train of Thought: Mathematics V1*. [Hugging Face dataset](https://huggingface.co/datasets/mattwesney/ToT-Math-V1). |
|
|
