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license: mit
task_categories:
- visual-question-answering
- image-text-to-text
language:
- en
pretty_name: Med Eval Data
size_categories:
- 10K<n<100K
---
# Med Eval Data
This dataset contains evaluation data for the **Med** project. Its data format is the same as [`Med2026/Med_training_data`](https://huggingface.co/datasets/Med2026/Med_training_data), and it can be loaded with the same codebase from [`GAIR-NLP/Med`](https://github.com/GAIR-NLP/Med).
## Overview
Each example is stored in the same JSON / parquet schema as the training data, with the following top-level fields:
- `images`
- `data_source`
- `prompt`
- `ability`
- `reward_model`
- `extra_info`
- `agent_name`
This means the dataset is directly compatible with the data loading pipeline used in the Med codebase.
## Compatibility
This dataset has the **same format** as [`Med2026/Med_training_data`](https://huggingface.co/datasets/Med2026/Med_training_data).
You can use the same loading logic and preprocessing pipeline from:
- [`GAIR-NLP/Med`](https://github.com/GAIR-NLP/Med)
No format conversion is required.
## Data Split by File Naming
The evaluation data is divided into two settings according to the file name:
- Files with `single_turn_agent` in the filename correspond to **evaluation without tool use**
- Files with `tool_agent` in the filename correspond to **evaluation with tool use**
In other words:
- `*single_turn_agent*` → without-tool evaluation
- `*tool_agent*` → with-tool evaluation
## Data Format
Each sample is a JSON object with the following structure:
```python
{
"images": [PIL.Image],
"data_source": "vstar_bench_single_turn_agent",
"prompt": [
{
"content": "<image>\nWhat is the material of the glove?\n(A) rubber\n(B) cotton\n(C) kevlar\n(D) leather\nAnswer with the option's letter from the given choices directly.",
"role": "user"
}
],
"ability": "direct_attributes",
"reward_model": {
"answer": "A",
"format_ratio": 0.0,
"ground_truth": "\\boxed{A}",
"length_ratio": 0.0,
"style": "multiple_choice",
"verifier": "mathverify",
"verifier_parm": {
"det_verifier_normalized": null,
"det_reward_ratio": {
"iou_max_label_first": null,
"iou_max_iou_first": null,
"iou_completeness": null,
"map": null,
"map50": null,
"map75": null
}
}
},
"extra_info": {
"answer": "A",
"data_source": "vstar_bench_single_turn_agent",
"id": "vstar_bench_0",
"image_path": "direct_attributes/sa_4690.jpg",
"question": "<image>\nWhat is the material of the glove?\n(A) rubber\n(B) cotton\n(C) kevlar\n(D) leather\nAnswer with the option's letter from the given choices directly.",
"split": "test",
"index": "0",
"prompt_length": null,
"tools_kwargs": {
"crop_and_zoom": {
"create_kwargs": {
"raw_query": "What is the material of the glove?\n(A) rubber\n(B) cotton\n(C) kevlar\n(D) leather\nAnswer with the option's letter from the given choices directly.",
"image": "PIL.Image"
}
}
},
"need_tools_kwargs": false
},
"agent_name": "single_turn_agent"
} |