Add MMSI-Video-Bench dataset

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - visual-question-answering
+  - video-text-to-text
+language:
+  - en
+tags:
+  - video
+  - spatial-intelligence
+  - multimodal
+  - benchmark
+size_categories:
+  - 1K<n<10K
+---
+
+# MMSI-Video-Bench
+
+A video-based spatial intelligence benchmark for evaluating Multimodal Large Language Models (MLLMs).
+
+## Dataset Description
+
+MMSI-Video-Bench tests models on:
+- Spatial reasoning
+- Motion understanding  
+- Planning and prediction
+- Cross-video reasoning
+
+## Dataset Structure
+
+```
+MMSI-Video-Bench/
+├── data/
+│   └── test-00000-of-00001.parquet   # 1106 samples
+├── frames.zip                         # Extracted video frames
+├── ref_images.zip                     # Reference images
+├── videos.zip                         # Original videos
+└── README.md
+```
+
+### Parquet Columns
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | string | Unique question ID (e.g., "question_0000") |
+| `type` | string | Question category |
+| `system_prompt` | string | System prompt |
+| `task_prompt` | string | Task description |
+| `user_prompt` | string | User question |
+| `format_prompt` | string | Answer format instructions |
+| `ori_question` | string | Original question text |
+| `options` | list[string] | Answer choices (A-F) |
+| `ground_truth` | string | Correct answer letter |
+| `video_list` | list[dict] | Video metadata (path, start, end, base_fps) |
+| `frames_list` | list[list[string]] | Paths to extracted frames per video |
+| `ref_images` | list[string] | Paths to reference images |
+
+## Usage
+
+```python
+import pandas as pd
+from datasets import load_dataset
+
+# Option 1: Load parquet directly
+df = pd.read_parquet("hf://datasets/oscarqjh/MMSI-Video-Bench/data/test-00000-of-00001.parquet")
+
+# Option 2: Load with datasets library
+dataset = load_dataset("oscarqjh/MMSI-Video-Bench")
+
+# Access a sample
+sample = dataset["train"][0]
+print(sample["id"])
+print(sample["ori_question"])
+print(sample["options"])
+print(sample["ground_truth"])
+
+# Video paths are relative to the extracted zip files
+# e.g., sample["video_list"][0]["path"] = "question_0000/video.mp4"
+# After extracting videos.zip: videos/question_0000/video.mp4
+```
+
+### Extracting Media Files
+
+```bash
+# Download and extract
+wget https://huggingface.co/datasets/oscarqjh/MMSI-Video-Bench/resolve/main/frames.zip
+wget https://huggingface.co/datasets/oscarqjh/MMSI-Video-Bench/resolve/main/ref_images.zip
+wget https://huggingface.co/datasets/oscarqjh/MMSI-Video-Bench/resolve/main/videos.zip
+
+unzip frames.zip -d frames/
+unzip ref_images.zip -d ref_images/
+unzip videos.zip -d videos/
+```
+
+## Citation
+
+Please cite the original MMSI-Video-Bench paper if you use this dataset.
+
+## License
+
+CC-BY-4.0

data/test-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5914ea0e10d908afa1334485e7d7a0bb4c7344fa4be63920bebac62506b1be47
+size 1553640

dataset_info.json

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+{
+  "description": "MMSI-Video-Bench: A video-based spatial intelligence benchmark for MLLMs",
+  "license": "cc-by-4.0",
+  "features": {
+    "id": {"dtype": "string"},
+    "type": {"dtype": "string"},
+    "system_prompt": {"dtype": "string"},
+    "task_prompt": {"dtype": "string"},
+    "user_prompt": {"dtype": "string"},
+    "format_prompt": {"dtype": "string"},
+    "ori_question": {"dtype": "string"},
+    "options": {"sequence": {"dtype": "string"}},
+    "ground_truth": {"dtype": "string"},
+    "video_list": {"sequence": {"struct": {"path": {"dtype": "string"}, "start": {"dtype": "float64"}, "end": {"dtype": "float64"}, "base_fps": {"dtype": "float64"}}}},
+    "frames_list": {"sequence": {"sequence": {"dtype": "string"}}},
+    "ref_images": {"sequence": {"dtype": "string"}}
+  }
+}