{
  "meta": {
    "pretty_name": "General-VQA",
    "dataset_id": "general_vqa",
    "paper_url": null,
    "tags": [
      "QA",
      "Custom",
      "MultiModal"
    ],
    "metrics": [
      "BLEU",
      "Rouge"
    ],
    "few_shot_num": 0,
    "eval_split": "test",
    "train_split": "",
    "subset_list": [
      "default"
    ],
    "description": "\n## Overview\n\nGeneral-VQA is a customizable visual question answering benchmark for evaluating multimodal models.\nIt supports OpenAI-compatible message format with flexible image/video input (local paths, URLs, or base64).\n\n## Task Description\n\n- **Task Type**: Visual Question Answering\n- **Input**: Images/videos + questions in OpenAI chat format\n- **Output**: Free-form text answer\n- **Flexibility**: Supports custom datasets via TSV/JSONL files\n\n## Key Features\n\n- OpenAI-compatible message format\n- Supports multiple image/video input methods (path, URL, base64)\n- Flexible evaluation with BLEU and Rouge metrics\n- Custom dataset support via local file loading\n- Extensible for various VQA use cases\n\n## Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n- Default metrics: **BLEU**, **Rouge** (Rouge-L-R as main score)\n- Evaluates on **test** split\n- See [User Guide](https://evalscope.readthedocs.io/en/latest/advanced_guides/custom_dataset/vlm.html) for dataset format\n",
    "prompt_template": "",
    "system_prompt": "",
    "few_shot_prompt_template": "",
    "aggregation": "mean",
    "extra_params": {},
    "sandbox_config": {},
    "category": "vlm"
  },
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    "total_samples": 0,
    "subset_stats": [],
    "prompt_length": {
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      "min": 0,
      "max": 0,
      "std": null
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    "target_length_mean": null,
    "computed_at": "2026-01-28T11:13:18.206294"
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  "sample_example": {},
  "readme": {
    "en": "# General-VQA\n\n\n## Overview\n\nGeneral-VQA is a customizable visual question answering benchmark for evaluating multimodal models.\nIt supports OpenAI-compatible message format with flexible image/video input (local paths, URLs, or base64).\n\n## Task Description\n\n- **Task Type**: Visual Question Answering\n- **Input**: Images/videos + questions in OpenAI chat format\n- **Output**: Free-form text answer\n- **Flexibility**: Supports custom datasets via TSV/JSONL files\n\n## Key Features\n\n- OpenAI-compatible message format\n- Supports multiple image/video input methods (path, URL, base64)\n- Flexible evaluation with BLEU and Rouge metrics\n- Custom dataset support via local file loading\n- Extensible for various VQA use cases\n\n## Evaluation Notes\n\n- Default configuration uses **0-shot** evaluation\n- Default metrics: **BLEU**, **Rouge** (Rouge-L-R as main score)\n- Evaluates on **test** split\n- See [User Guide](https://evalscope.readthedocs.io/en/latest/advanced_guides/custom_dataset/vlm.html) for dataset format\n\n\n## Properties\n\n| Property | Value |\n|----------|-------|\n| **Benchmark Name** | `general_vqa` |\n| **Dataset ID** | `general_vqa` |\n| **Paper** | N/A |\n| **Tags** | `Custom`, `MultiModal`, `QA` |\n| **Metrics** | `BLEU`, `Rouge` |\n| **Default Shots** | 0-shot |\n| **Evaluation Split** | `test` |\n\n\n## Data Statistics\n\n*Statistics not available.*\n\n## Sample Example\n\n*Sample example not available.*\n\n## Prompt Template\n\n*No prompt template defined.*\n\n## Usage\n\n### Using CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets general_vqa \\\n    --limit 10  # Remove this line for formal evaluation\n```\n\n### Using Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['general_vqa'],\n    limit=10,  # Remove this line for formal evaluation\n)\n\nrun_task(task_cfg=task_cfg)\n```\n\n\n",
    "zh": "# General-VQA\n\n\n## 概述\n\nGeneral-VQA 是一个可自定义的视觉问答（Visual Question Answering）基准测试，用于评估多模态模型。  \n它支持 OpenAI 兼容的消息格式，并允许灵活的图像/视频输入方式（本地路径、URL 或 base64 编码）。\n\n## 任务描述\n\n- **任务类型**：视觉问答（Visual Question Answering）\n- **输入**：图像/视频 + 以 OpenAI 聊天格式表示的问题\n- **输出**：自由格式的文本答案\n- **灵活性**：通过 TSV/JSONL 文件支持自定义数据集\n\n## 主要特性\n\n- OpenAI 兼容的消息格式\n- 支持多种图像/视频输入方式（路径、URL、base64）\n- 使用 BLEU 和 Rouge 指标进行灵活评估\n- 通过本地文件加载支持自定义数据集\n- 可扩展以适用于各种 VQA 应用场景\n\n## 评估说明\n\n- 默认配置使用 **0-shot** 评估\n- 默认指标：**BLEU**、**Rouge**（以 Rouge-L-R 为主要得分）\n- 在 **test** 数据划分上进行评估\n- 数据集格式详见 [用户指南](https://evalscope.readthedocs.io/zh-cn/latest/advanced_guides/custom_dataset/vlm.html)\n\n## 属性\n\n| 属性 | 值 |\n|----------|-------|\n| **基准测试名称** | `general_vqa` |\n| **数据集ID** | `general_vqa` |\n| **论文** | N/A |\n| **标签** | `Custom`, `MultiModal`, `QA` |\n| **指标** | `BLEU`, `Rouge` |\n| **默认示例数（Shots）** | 0-shot |\n| **评估数据划分** | `test` |\n\n\n## 数据统计\n\n*统计数据不可用。*\n\n## 样例示例\n\n*样例示例不可用。*\n\n## 提示模板\n\n*未定义提示模板。*\n\n## 使用方法\n\n### 使用 CLI\n\n```bash\nevalscope eval \\\n    --model YOUR_MODEL \\\n    --api-url OPENAI_API_COMPAT_URL \\\n    --api-key EMPTY_TOKEN \\\n    --datasets general_vqa \\\n    --limit 10  # 正式评估时请删除此行\n```\n\n### 使用 Python\n\n```python\nfrom evalscope import run_task\nfrom evalscope.config import TaskConfig\n\ntask_cfg = TaskConfig(\n    model='YOUR_MODEL',\n    api_url='OPENAI_API_COMPAT_URL',\n    api_key='EMPTY_TOKEN',\n    datasets=['general_vqa'],\n    limit=10,  # 正式评估时请删除此行\n)\n\nrun_task(task_cfg=task_cfg)\n```",
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  "updated_at": "2026-05-18T10:23:18.721476",
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