Create README.md

README.md

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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`].
+
+## 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"
+}