ChartQA

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# Large-scale Multi-modality Models Evaluation Suite

> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`

🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)

# This Dataset

This is a formatted version of [ChartQA](https://github.com/vis-nlp/ChartQA). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.

```
@article{masry2022chartqa,
title={ChartQA: A benchmark for question answering about charts with visual and logical reasoning},
author={Masry, Ahmed and Long, Do Xuan and Tan, Jia Qing and Joty, Shafiq and Hoque, Enamul},
journal={arXiv preprint arXiv:2203.10244},
year={2022}
}
```

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# 大规模多模态模型评测套件
> 借助`lmms-eval`加速大规模多模态模型（Large-scale Multi-modality Models，简称LMMs）的研发

🏠 [主页](https://lmms-lab.github.io/) | 📚 [文档](docs/README.md) | 🤗 [Huggingface数据集](https://huggingface.co/lmms-lab)

# 本数据集

本数据集是[ChartQA](https://github.com/vis-nlp/ChartQA)的格式化版本，可应用于我们的`lmms-eval`流水线中，实现大规模多模态模型的一键评测。

@article{masry2022chartqa,
title={ChartQA：面向图表问答的视觉与逻辑推理基准测试集},
author={Masry, Ahmed and Long, Do Xuan and Tan, Jia Qing and Joty, Shafiq and Hoque, Enamul},
journal={arXiv预印本 arXiv:2203.10244},
year={2022}
}

搜集汇总

背景与挑战

背景概述

ChartQA是一个用于图表问答的基准数据集，专注于评估视觉和逻辑推理能力。该数据集由lmms-lab发布，采用Apache License 2.0许可，并已格式化以支持大规模多模态模型的一键评估，便于在lmms-eval管道中使用。