README.md

---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: class
    dtype: string
  - name: id
    dtype: string
  - name: question
    dtype: string
  - name: option
    dtype: string
  - name: answer
    dtype: string
  - name: task_class
    dtype: string
  - name: Attributes
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 82349062.411
    num_examples: 1913
  download_size: 230897223
  dataset_size: 82349062.411
task_categories:
- image-text-to-text
tags:
- geometry
- mathematical-reasoning
- multimodal
---

This dataset is designed for research in **Deep Learning for Geometry Problem Solving (DL4GPS)** and accompanies the survey paper [A Survey of Deep Learning for Geometry Problem Solving](https://huggingface.co/papers/2507.11936). It aims to provide a structured resource for evaluating and training AI models, particularly multimodal large language models (MLLMs), on mathematical reasoning tasks involving geometric contexts.

The dataset provides a collection of geometry problems, each consisting of a textual question and a corresponding image.

For a continuously updated reading list of papers on Deep Learning for Geometry Problem Solving, refer to the [official GitHub repository](https://github.com/majianz/gps-survey).

## Data Structure

Each problem instance in the dataset includes the following fields:

-   `class`: The category of the geometry problem.
-   `id`: A unique identifier for each problem.
-   `question`: The textual description of the geometry problem.
-   `option`: Multiple-choice options for the answer, if applicable.
-   `answer`: The correct answer to the geometry problem.
-   `task_class`: A classification of the task involved.
-   `Attributes`: Additional attributes or metadata about the problem.
-   `image`: The image of the geometric diagram associated with the problem.