upload readme

README.md

@@ -1,4 +1,25 @@
 ---
+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:
@@ -28,6 +49,42 @@ dataset_info:
   download_size: 51634467
   dataset_size: 127629209.0
 ---
-# Dataset Card for "chartve_dataset"
+# 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:
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+```
+@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}
+}    
+```