Initial webpage commit

Author: NipElement

.gitignore

@@ -1,70 +1,7 @@
-# VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
+# VisCoder
+Homepage of VisCoder, an open-source large language model fine-tuned for Python visualization code generation and iterative self-correction. 
 
-[**🌐 Project Page**](https://tiger-ai-lab.github.io/VisCoder) | [**📖 arXiv**](https://arxiv.org/abs/2506.03930) | [**🤗 VisCode-200K Dataset**](https://huggingface.co/datasets/TIGER-Lab/VisCode-200K) | [**🤗 VisCoder-3B**](https://huggingface.co/TIGER-Lab/VisCoder-3B) | [**🤗 VisCoder-7B**](https://huggingface.co/TIGER-Lab/VisCoder-7B)
+This website is adapted from [MathVista](https://nerfies.github.io) and [MMMU](https://mmmu-benchmark.github.io/).
 
-This repository provides the training and evaluation code for our paper: 
-> **VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation**  
-> Yuansheng Ni, Ping Nie, Kai Zou, Xiang Yue, Wenhu Chen
-
----
-
-## 🔔 News
-
-- **🔥 [2025-06-05] VisCoder and VisCode-200K are now publicly released! Check out our [paper](https://arxiv.org/abs/2506.03930) and [collections](https://huggingface.co/collections/TIGER-Lab/viscoder-6840333efe87c4888bc93046).**
----
-
-## 🧠 Introduction
-
-**VisCoder** is an open-source large language model fine-tuned for **Python visualization code generation and iterative self-correction**. It is trained on **VisCode-200K**, a large-scale instruction-tuning dataset tailored for executable plotting tasks and runtime-guided revision.
-
-VisCoder addresses a core challenge in data analysis: generating Python code that produces not only syntactically correct, but also **visually meaningful plots**. Unlike general code generation tasks, visualization requires grounding across **natural language instructions, data structures**, and **rendered visual outputs**.
-
-To enable this, **VisCode-200K** includes:
-- ✅ **150K+ executable visualization examples**, validated through runtime checks and paired with plot images.
-- 🔁 **45K multi-turn correction dialogues** from the Code-Feedback dataset, providing supervision for fixing faulty code based on execution feedback.
-
-![Alt text](assets/pipeline.png)
-
-We further propose a **self-debug evaluation protocol**, simulating real-world developer workflows through multi-round error correction. VisCoder is benchmarked on **PandasPlotBench** against GPT-4o, GPT-4o-mini, Qwen, and LLaMA, demonstrating robust performance and strong recovery from execution failures.
-
----
-## 📊 Main Results on PandasPlotBench
-
-We evaluate VisCoder on **PandasPlotBench**, a benchmark for executable Python visualization code generation across three libraries: **Matplotlib**, **Seaborn**, and **Plotly**. The figure below summarizes model performance in terms of execution success and GPT-4o-judged alignment scores.
-
-![Alt text](assets/main_results.png)
-
-> With **self-debug**, **VisCoder-7B** achieves over **90% execution pass rate** on both **Matplotlib** and **Seaborn**, outperforming strong open-source baselines and approaching GPT-4o performance on multiple libraries.
-
----
-
-## 🛠️ Training & Evaluation
-
-We provide both training and evaluation scripts for VisCoder.
-
-- 📦 **Training** is performed using the [ms-swift](https://github.com/modelscope/swift) framework with full-parameter supervised fine-tuning on VisCode-200K.
-- 📊 **Evaluation** is based on the [PandasPlotBench](https://github.com/JetBrains-Research/PandasPlotBench). We **augment the original evaluation** with an additional **Execution Pass Rate** metric and introduce a new **self-debug evaluation mode** that allows models to revise failed generations over multiple rounds.
-
-See the following folders for details:
-
-- [`train/`](./train): Training scripts and configurations based on ms-swift.
-- [`eval/`](./eval): Evaluation scripts adapted from PandasPlotBench with our self-debug extension.
-
-## Contact
-- Yuansheng Ni: [email protected]
-- Wenhu Chen: [email protected]
-
-## 📖 Citation
-
-**BibTeX:**
-```bibtex
-@misc{ni2025viscoderfinetuningllmsexecutable,
-      title={VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation}, 
-      author={Yuansheng Ni and Ping Nie and Kai Zou and Xiang Yue and Wenhu Chen},
-      year={2025},
-      eprint={2506.03930},
-      archivePrefix={arXiv},
-      primaryClass={cs.SE},
-      url={https://arxiv.org/abs/2506.03930}, 
-}
-```
+# Website License
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.