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README.md

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-# CLIP Model for Chart Understanding
+# Data for CLIP Training on Chart Task
 
-This repository contains the CLIP model implementation from our paper "[On the Perception Bottleneck of VLMs for Chart Understanding](https://arxiv.org/abs/2503.18435)".
+This repository contains the CLIP Training data implementation from our paper "[On the Perception Bottleneck of VLMs for Chart Understanding](https://arxiv.org/abs/2503.18435)".
 
-## Overview
 
-This CLIP model is specifically trained to address the perception bottleneck in Vision Language Models (VLMs) when processing and understanding charts and visualizations. Our work explores and aims to improve how CLIP effect its LVLMs.
-
-
-## Model Details
-
-
-- Model architecture: trained from openai/clip-vit-large-patch14-336
-- Training data: from our collected and synthetic hard negatives chart data([Vision4Chart Dataset](https://huggingface.co/datasets/Junteng/Vision4Chart))
-- Training method: NegCLIP Training
+## Data Details
 
+-Data Source: Mainly chart tasks data like ChartQA, FigureQA, and DVQA.
+-Data overview: Each data contains image, a correct caption and wrong caption.
 
 ## Citation
 
-If you find this model useful in your research, please consider citing our paper:
+If you find this data useful in your research, please consider citing our paper:
 
 ```bibtex
 @misc{liu2025perceptionbottleneckvlmschart,