README.md

metadata

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 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:

from datasets import load_dataset

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