---
dataset_info:
  features:
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: images
    list: string
  - name: id
    dtype: string
  splits:
  - name: NASPO_MMK12
    num_examples: 2000
  - name: hiyouga_geometry3k
    num_examples: 601
  - name: AI4Math_MathVerse
    num_examples: 2180
  - name: AI4Math_MathVista
    num_examples: 1000
  - name: We_Math
    num_examples: 1740
  - name: MMMU_MMMU_Pro
    num_examples: 1730
  - name: AI4Math_MathVerse_vision_dependent
    num_examples: 1308
  - name: lscpku_LogicVista
    num_examples: 447
configs:
- config_name: default
  data_files:
  - split: NASPO_MMK12
    path: data/NASPO_MMK12-*
  - split: hiyouga_geometry3k
    path: data/hiyouga_geometry3k-*
  - split: AI4Math_MathVerse
    path: data/AI4Math_MathVerse-*
  - split: AI4Math_MathVista
    path: data/AI4Math_MathVista-*
  - split: We_Math
    path: data/We_Math-*
  - split: MMMU_MMMU_Pro
    path: data/MMMU_MMMU_Pro-*
  - split: AI4Math_MathVerse_vision_dependent
    path: data/AI4Math_MathVerse_vision_dependent-*
  - split: lscpku_LogicVista
    path: data/lscpku_LogicVista-*
task_categories:
- image-text-to-text
language:
- en
library_name: datasets
tags:
- multimodal
- reasoning
- evaluation
---

# NAS-PO evaluation benchmark

This repository contains the eight evaluation configurations reported by
NAS-PO. They are prepared from MMK12, Geometry3K, MathVerse, MathVista,
We-Math, MMMU-Pro, and LogicVista.

The release contains eight Hugging Face dataset splits under `data/` and one
matching image ZIP for each split. The NAS-PO preprocessing command converts
the selected splits to ShareGPT JSON and extracts the archives into its local
`data/images/` directory. Image references are portable paths relative to
that materialized data root.

## Usage

```python
from datasets import load_dataset

mmk12 = load_dataset("ANke121/NASPO_eval", split="NASPO_MMK12")
assert len(mmk12) == 2000
```

The complete inference and Accuracy@8 workflow is provided by `NASPO-Eval`
in the NAS-PO source repository.

## Acknowledgements

The public
[PAPO](https://github.com/MikeWangWZHL/PAPO) release was used as a reference
for organizing these files and the evaluation workflow. The individual
datasets and benchmarks remain credited to their original creators and are
subject to their respective licenses and terms.