README.md

---
language:
- en

license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
tags:
- chart
- plot
- chart-to-text
- vistext
- statista
- pew
- chart-visual-entailment
- chart-understanding
- chart-captioning
- chart-summarization
- document-image

configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
dataset_info:
  features:
  - name: image
    dtype: string
  - name: label
    dtype: string
  - name: manipulation_type
    dtype: string
  - name: dataset
    dtype: string
  - name: sentence
    dtype: string
  splits:
  - name: train
    num_bytes: 118229163.0
    num_examples: 522531
  - name: dev
    num_bytes: 9400046.0
    num_examples: 36002
  download_size: 51634467
  dataset_size: 127629209.0
---
# Dataset Card for ChartVE's Training Data

- [Dataset Description](https://huggingface.co/datasets/khhuang/ChartVE/blob/main/README.md#dataset-description)
- [Paper Information](https://huggingface.co/datasets/khhuang/ChartVE/blob/main/README.md#paper-information)
- [Citation](https://huggingface.co/datasets/khhuang/ChartVE/blob/main/README.md#citation)

## Dataset Description

[ChartVE](https://huggingface.co/khhuang/chartve) (Chart Visual Entailment) is a visual entailment model introduced in the paper "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning" for evaluating the factuality of a generated caption sentence with regard to the input chart. The model takes in a chart figure and a caption sentence as input, and outputs an entailment probability. This repository hosts the training and validation data for ChartVE.


## Paper Information

- Paper: https://arxiv.org/abs/2312.10160
- Code: https://github.com/khuangaf/CHOCOLATE/
- Project: https://khuangaf.github.io/CHOCOLATE


## Citation

If you use the **ChartVE** dataset/model in your work, please kindly cite the paper using this BibTeX:

```
@misc{huang-etal-2023-do,
    title = "Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning",
    author = "Huang, Kung-Hsiang  and
      Zhou, Mingyang and
      Chan, Hou Pong  and
      Fung, Yi R. and
      Wang, Zhenhailong and
      Zhang, Lingyu and
      Chang, Shih-Fu and
      Ji, Heng",
    year={2023},
    eprint={2312.10160},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}    
```