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---
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")
``` |