Add pipeline tag

This PR adds the `pipeline_tag` to the model card, ensuring the model can be found at https://huggingface.co/models?pipeline_tag=video-text-to-text.

README.md

@@ -1,22 +1,22 @@
 ---
 library_name: transformers
 license: mit
+pipeline_tag: video-text-to-text
 ---
 
-
-
 <a href='https://arxiv.org/abs/2501.13919v1'><img src='https://img.shields.io/badge/arXiv-paper-red'></a><a href='https://ruili33.github.io/tpo_website/'><img src='https://img.shields.io/badge/project-TPO-blue'></a>
 <a href='https://huggingface.co/collections/ruili0/temporal-preference-optimization-67874b451f65db189fa35e10'><img src='https://img.shields.io/badge/model-checkpoints-yellow'></a> 
 <a href='https://github.com/ruili33/TPO'><img src='https://img.shields.io/badge/github-repository-purple'></a> 
 <img src="cvpr_figure_TPO.png"></img>
 # LLaVA-Video-7B-Qwen2-TPO
 
-LLaVA-Video-7B-Qwen2-TPO, introduced by paper [Temporal Preference Optimization for Long-form Video Understanding](https://arxiv.org/abs/2501.13919v1), optimized
+LLaVA-Video-7B-Qwen2-TPO, introduced by paper [Temporal Preference Optimization for Long-form Video Understanding](https://huggingface.co/papers/2501.13919v1), optimized
 by temporal preference based on LLaVA-Video-7B-Qwen2. The LLaVA-Video-7B-Qwen2-TPO model establishes state-of-the-art performance across a range of 
 benchmarks, demonstrating an average performance improvement of 1.5% compared to LLaVA-Video-7B. 
 Notably, it emerges as the leading 7B parameter model on the Video-MME benchmark.
 
-
+Project page: https://ruili33.github.io/tpo_website/
+Code: https://github.com/ruili33/TPO
 
 ## Evaluation Results
 | **Model**                           | **Size** | **LongVideoBench** | **MLVU**  | **VideoMME (Average)** |
@@ -121,4 +121,4 @@ This project utilizes certain datasets and checkpoints that are subject to their
 
 [1]. Liu, Z., Zhu, L., Shi, B., Zhang, Z., Lou, Y., Yang, S., ... & Lu, Y. (2024). NVILA: Efficient Frontier Visual Language Models. arXiv preprint arXiv:2412.04468.
 
-[2]. Zhang, Y., Wu, J., Li, W., Li, B., Ma, Z., Liu, Z., & Li, C. (2024). Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713.
+[2]. Zhang, Y., Wu, J., Li, W., Li, B., Ma, Z., Liu, Z., & Li, C. (2024). Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713.