Improve model card: add metadata, paper link, and GitHub repository

This PR improves the model card for **ST-Evidence-7B** by:
- Adding the `video-text-to-text` pipeline tag to make the model discoverable on the Hub.
- Specifying the `cc-by-nc-4.0` license in the YAML metadata.
- Linking the model card to the corresponding paper: [Evidence-Backed Video Question Answering](https://huggingface.co/papers/2607.11862).
- Linking to the official GitHub repository and relevant datasets on the Hub.
- Including the official BibTeX citation.

README.md

@@ -1,14 +1,38 @@
+---
+license: cc-by-nc-4.0
+pipeline_tag: video-text-to-text
+---
+
 # ST-Evidence-7B
 
 We propose **Evidence-Backed Video Question Answering** (E-VQA), a task where multimodal models are designed to jointly produce a semantic **textual answer** and associated spatiotemporal evidence. This evidence includes **temporal segments** and **dense, tracked object segmentation masklets**. A masklet is defined as a temporal sequence of object segmentation masks tracked over time.
 
-Our model was fine-tuned from UniPixel, which is built upon QWen2.5-VL and SAM 2.1. UniPixel is a unified model designed to handle both video question answering and mask generation.
+This model is presented in the paper [Evidence-Backed Video Question Answering](https://huggingface.co/papers/2607.11862) (ECCV 2026).
+
+ST-Evidence-7B was fine-tuned from UniPixel, which is built upon Qwen2.5-VL and SAM 2.1. UniPixel is a unified model designed to handle both video question answering and mask generation.
 
 This was released for research purposes only, in support of the academic paper *Evidence-Backed Video Question Answering*.
 
+## Resources
+
+- **Repository:** [SalesforceAIResearch/EVQA](https://github.com/SalesforceAIResearch/EVQA)
+- **Paper:** [Evidence-Backed Video Question Answering](https://huggingface.co/papers/2607.11862)
+- **Benchmark (ST-Evidence):** [Salesforce/ST-Evidence-Bench](https://huggingface.co/datasets/Salesforce/ST-Evidence-Bench)
+- **SFT Dataset (ST-Evidence-Instruct):** [Salesforce/ST-Evidence-Instruct](https://huggingface.co/datasets/Salesforce/ST-Evidence-Instruct)
 
 ## License
 
 CC-BY-NC 4.0
 
+## Citation
+
+If you find this work useful for your research, please cite our paper:
 
+```bibtex
+@inproceedings{wang2026evidence,
+  title={Evidence-Backed Video Question Answering},
+  author={Wang, Shijie and Zhou, Honglu and Wang, Ziyang and Xu, Ran and Xiong, Caiming and Savarese, Silvio and Sun, Chen and Niebles, Juan Carlos},
+  booktitle={European Conference on Computer Vision (ECCV)},
+  year={2026}
+}
+```