Update README.md

README.md

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-license: apache-2.0
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+# PPTAnimation_Test Dataset Card
+
+## 1. Overview
+**PPTAnimation_Test** comprises **1,000** synthetic short videos (< 15 s each) of PowerPoint slide animations, paired one-to-one with human-written captions.  
+- `Videos/`: MP4 clips named `video_0001.mp4` … `video_1000.mp4`.  
+- `Captions/`: Plain-text files (`video_0001.txt`, etc.) that store the **ground-truth natural-language descriptions** of each video.
+
+The dataset is ideal for **vision–language model (VLM)** fine-tuning or evaluation, and it also supports tasks such as **video captioning** and **video understanding**.
+
+## 2. Directory Structure
+```bash
+PPTAnimation_Test/
+├── Videos/
+│   ├── video_0001.mp4
+│   ├── video_0002.mp4
+│   └── ...
+└── Captions/
+    ├── video_0001.txt
+    ├── video_0002.txt
+    └── ...
+```
+
+## 3. Tasks & Applications
+- Vision–language alignment: video–text retrieval, cross-modal understanding
+
+- Video caption generation: produce textual descriptions from animation videos
+
+- VLM fine-tuning / benchmarking: assess a model’s ability to understand PPT animations
+
+## 4. License
+This dataset is released under the Apache License 2.0. Please comply with its terms of use.
+
+## 5. Citation
+If you use the dataset in academic work, please cite the following paper:
+```bibtex
+@misc{jiang2025animationneedsattentionholistic,
+      title        = {Animation Needs Attention: A Holistic Approach to Slides Animation Comprehension with Visual-Language Models},
+      author       = {Yifan Jiang and Yibo Xue and Yukun Kang and Pin Zheng and Jian Peng and Feiran Wu and Changliang Xu},
+      year         = {2025},
+      eprint       = {2507.03916},
+      archivePrefix= {arXiv},
+      primaryClass = {cs.AI},
+      url          = {https://arxiv.org/abs/2507.03916},
+}
+```
+## 6. Usage Example
+
+```python
+from modelscope.msdatasets import MsDataset
+
+dataset = MsDataset.load(
+    dataset_name='jyf9774/PPTAnimation_Test',
+    namespace='jyf9774',
+    split='train'  # no official split; use 'train' or None
+)
+
+sample = dataset[0]
+print(sample['text'])        # Caption text
+sample['video'].display()    # Preview the video in a notebook
+```