| --- |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "images.zip" |
| task_categories: |
| - image-captioning |
| - image-text-retrieval |
| language: |
| - en |
| tags: |
| - mathematics |
| - multimodal |
| - benchmark |
| - wikipedia |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # MAT-VB: MAthematical Text-Vision Benchmark |
|
|
| 🏆 **Best Resource Paper Award** — *2025 ACM/IEEE Joint Conference on Digital Libraries (JCDL)* |
|
|
| Official Hugging Face dataset repository for **MAT-VB**, a benchmark built on Wikipedia math images designed to advance research in multi-modal mathematical tasks, including visual-text retrieval and math image captioning. |
|
|
| --- |
|
|
| ## 🔗 Links |
|
|
| * **GitHub Repository:** [AIIRLab/MATVB](https://github.com/AIIRLab/MATVB) |
| * **Paper (IEEE Xplore):** [MAT-VB: MAthematical Text-Vision Benchmark](https://ieeexplore.ieee.org/abstract/document/11363711) |
|
|
| --- |
|
|
| ## 📁 Dataset Contents |
|
|
| MAT-VB provides a curated collection of mathematical visual data from Wikipedia for evaluating multimodal AI models: |
|
|
| * **`images.zip`**: Contains all ~4,000 Wikipedia math image files (216 MB uncompressed archive). |
| * **Metadata & Annotations**: Includes descriptive captions, query-image mappings, and split indexes for training and evaluation pipelines. |
|
|
| --- |
|
|
| ## 🚀 Usage with Python `datasets` |
|
|
| Hugging Face automatically extracts and parses `images.zip` when loading through the `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the dataset directly |
| dataset = load_dataset("AIIRLab/MATVB") |
| |
| # Access a sample |
| example = dataset["train"][0] |
| image = example["image"] |
| caption = example.get("caption", "") |
