Create README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+- question-answering
+language:
+- en
+tags:
+- math
+---
+
+## Overview
+VCBench provides a standardized framework for evaluating vision-language models. This document outlines the procedures for both standard evaluation and GPT-assisted evaluation of your model's outputs.
+
+## 1. Standard Evaluation
+
+### 1.1 Output Format Requirements
+Models must produce outputs in JSONL format with the following structure:
+```
+{"id": <int>, "pred_answer": "<answer_letter>"}
+{"id": <int>, "pred_answer": "<answer_letter>"}
+...
+```
+
+**Example File (`submit.jsonl`):**
+```json
+{"id": 1, "pred_answer": "A"}
+{"id": 2, "pred_answer": "B"}
+{"id": 3, "pred_answer": "C"}
+```
+
+### 1.2 Evaluation Procedure
+1. Ensure your predictions file follows the specified format
+2. Run the evaluation script:
+   ```bash
+   python evaluate_vcbench.py -p ./path/to/predictions.jsonl -g ./path/to/VCBench_with_answer.json
+   ```
+`VCBench_with_answer.json` is the ground truth file which can be downloaded from [here](https://huggingface.co/datasets/cloudcatcher2/VCBench/resolve/main/VCBench_with_answer.json).
+
+## 2. GPT-Assisted Evaluation
+
+### 2.1 Output Format Requirements
+For natural language responses, use this JSONL format:
+```
+{"id": <int>, "pred_answer": "<natural_language_response>"}
+{"id": <int>, "pred_answer": "<natural_language_response>"}
+...
+```
+
+**Example File (`nl_predictions.jsonl`):**
+```json
+{"id": 1, "pred_answer": "The correct answer is A"}
+{"id": 2, "pred_answer": "After careful analysis, option B appears correct"}
+{"id": 3, "pred_answer": "C is the right choice"}
+```
+
+### 2.2 Environment Setup
+Set your Dashscope API key:
+   ```bash
+   export DASHSCOPE_KEY="your_api_key_here"
+   ```
+
+### 2.3 Evaluation Procedure
+```bash
+python evaluate_vcbench_by_gpt.py -p ./path/to/nl_predictions.jsonl -g ./path/to/VCBench_with_answer.json
+```
+
+## 3. Expected Output
+Both evaluation scripts will provide:
+- Overall accuracy percentage
+- Per-question-type accuracy breakdown
+- Progress updates during evaluation
+
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```bibtex
+@misc{wong2025vcbench
+  author    = {Zhikai Wang and Jiashuo Sun and Wenqi Zhang and Zhiqiang Hu and Xin Li and Fan Wang and Deli Zhao},
+  title     = {Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency},
+  year      = {2025},
+  eprint    = {2504.18589},
+  archivePrefix = {arxiv},
+  primaryClass  = {cs.CV},
+  url       = {https://arxiv.org/abs/2504.18589}
+}
+```
+
+## Dataset Card Authors
+
+- [Zhikai Wang](https://cloudcatcher888.github.io/): [email protected]
+- [Jiashuo Sun](https://gasolsun36.github.io/): [email protected]