Add pipeline tag

This PR ensures that the model shows up at https://huggingface.co/models?pipeline_tag=image-text-to-text&sort=trending.

README.md

@@ -1,11 +1,13 @@
 ---
-license: apache-2.0
 library_name: perception-encoder
+license: apache-2.0
+pipeline_tag: image-text-to-text
 ---
+
 # Model Details
 
-[\[📃 Tech Report\]](https://arxiv.org/abs/2504.13181)
-[\[📂 Github\]](https://github.com/facebookresearch/perception_models/)
+[\\[📃 Tech Report\\]](https://arxiv.org/abs/2504.13181)
+[\\[📂 Github\\]](https://github.com/facebookresearch/perception_models/)
 
 Perception Encoder (PE) is a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. It was introduced in "[Perception Encoder: The best visual embeddings
 are not at the output of the network](https://ai.meta.com/research/publications/perception-encoder-the-best-visual-embeddings-are-not-at-the-output-of-the-network/)".
@@ -61,5 +63,4 @@ If you find our code useful for your research, please consider citing:
       author={Jang Hyun Cho and Andrea Madotto and Effrosyni Mavroudi and Triantafyllos Afouras and Tushar Nagarajan and Muhammad Maaz and Yale Song and Tengyu Ma and Shuming Hu and Hanoona Rasheed and Peize Sun and Po-Yao Huang and Daniel Bolya and Suyog Jain and Miguel Martin and Huiyu Wang and Nikhila Ravi and Shashank Jain and Temmy Stark and Shane Moon and Babak Damavandi and Vivian Lee and Andrew Westbury and Salman Khan and Philipp Kr\"{a}henb\"{u}hl and Piotr Doll{\'a}r and Lorenzo Torresani and Kristen Grauman and Christoph Feichtenhofer},
       journal={arXiv},
       year={2025}
-    }
-    
+    }