---
dataset_info:
  features:
  - name: problem
    dtype: string
  - name: solution
    dtype: string
  - name: answer
    dtype: string
  - name: problem_type
    dtype: string
  - name: question_type
    dtype: string
  - name: problem_is_valid
    dtype: string
  - name: solution_is_valid
    dtype: string
  - name: source
    dtype: string
  - name: synthetic
    dtype: bool
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 710189273
    num_examples: 520811
  download_size: 329568716
  dataset_size: 710189273
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# NuminaMath 1.5

This dataset is a curated subset of the original [AI-MO/NuminaMath-1.5](https://huggingface.co/datasets/AI-MO/NuminaMath-1.5) dataset.

##  Filtering Criteria

This subset was created by applying the following three conditions to the 'train' split of the original dataset:

1.  The problem is valid (`problem_is_valid` == 'Yes')
2.  The solution is valid (`solution_is_valid` == 'Yes')
3.  The problem is not synthetic (`synthetic` == False)

This process resulted in a dataset of **520k** examples, compared to the original **896k** examples.

## Data Fields

The data fields are inherited from the original dataset and include:
* `problem`: The mathematical problem statement in LaTeX.
* `solution`: A step-by-step, Chain-of-Thought style solution.
* `answer`: The final answer to the problem.
* `problem_type`: The mathematical domain (e.g., Algebra, Geometry).
* `question_type`: The style of the problem (e.g., proof, math-word-problem).
* `source`: The origin of the problem (e.g., olympiads, cn_k12).

## How to Use

The dataset can be loaded easily using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("jimneussl/NuminaMath-Clean")
```