Improve model card: Add pipeline tag, library name, and detailed description

This PR significantly enhances the model card for the LCO-Embedding model by adding essential metadata and a more detailed description of its capabilities.

Specifically:
- Adds `pipeline_tag: feature-extraction` to improve discoverability on the Hugging Face Hub, as this model is designed for omnimodal representation learning.
- Adds `library_name: transformers`, indicating compatibility with the Hugging Face Transformers library, as evidenced by the `config.json` and `tokenizer_config.json` files.
- Updates the content to provide a comprehensive overview of the model, including its key contributions (LCO-Embedding framework, Generation-Representation Scaling Law, SeaDoc task), and incorporates evaluation results with illustrative figures directly from the GitHub repository.
- Explicitly links to the paper and the GitHub repository for easy access to more information and code.

Please review and merge this PR if everything looks good.

README.md

@@ -1,21 +1,52 @@
 ---
 license: apache-2.0
+pipeline_tag: feature-extraction
+library_name: transformers
 ---
 
-We are thrilled to release LCO-Embed - a language-centric omnimodal representation learning framework and the LCO-Embedding model families!
+# LCO-Embedding: Scaling Language-Centric Omnimodal Representation Learning
 
-Models and datasets: https://huggingface.co/LCO-Embedding
+We are thrilled to release LCO-Embedding - a language-centric omnimodal representation learning framework and the LCO-Embedding model families!
 
-Github: https://github.com/LCO-Embedding/LCO-Embedding
+This model implements the framework presented in the paper [Scaling Language-Centric Omnimodal Representation Learning](https://huggingface.co/papers/2510.11693), accepted by NeurIPS 2025.
 
-```
-@misc{xiao2025scalinglanguagecentricomnimodalrepresentation,
-      title={Scaling Language-Centric Omnimodal Representation Learning}, 
-      author={Chenghao Xiao and Hou Pong Chan and Hao Zhang and Weiwen Xu and Mahani Aljunied and Yu Rong},
-      year={2025},
-      eprint={2510.11693},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2510.11693}, 
+**Project Page:** https://huggingface.co/LCO-Embedding
+**Github Repository:** https://github.com/LCO-Embedding/LCO-Embedding
+
+## Overview
+
+We introduce **LCO-Embedding**, a language-centric omnimodal representation learning method and the LCO-Embedding model families, setting a new state-of-the-art on [MIEB](https://huggingface.co/blog/isaacchung/introducing-mieb) (Massive Image Embedding Benchmark), while supporting audio and videos.
+
+This work also introduces the **Generation-Representation Scaling Law**, connecting models' generative capabilities and their representation upper bound. Furthermore, we introduce **SeaDoc**, a challenging visual document retrieval task in Southeast Asian languages, and show that continual generative pretraining before contrastive learning raises the representation upper bound.
+
+<div align='center'><img src="https://cdn-uploads.huggingface.co/production/uploads/604f67ef0fe8ff3ec13d71ef/4Wd8fDFBdT6GxqN6-KzZN.png" alt="overview" width="100%"/></div>
+
+## Evaluation Results
+
+We evaluate LCO-Embedding with state-of-the-art embedding models, including E5-V, Voyage Multimodal 3, mmE5, and GME, on a MIEB-Lite benchmark (51 tasks) broken down by task categories.
+
+<div align='center'><img src="https://cdn-uploads.huggingface.co/production/uploads/63108cc834c7d77420b0fd68/63WBsKh57HbNwwe3bZ-oZ.png" alt="mieb_lite" width="100%"/></div>
+
+Performance and efficiency comparisons of different training strategies using 3B and 7B variants of Qwen2.5-VL backbones.
+
+<div align='center'><img src="https://github.com/LCO-Embedding/LCO-Embedding/raw/main/assets/lora_ablation.png" alt="lora_ablation" width="100%"/></div>
+
+Scaling relationship between generation benchmark performance (X-axis) and representation benchmark performance after language-centric contrastive learning (Y-axis).
+
+<div align='center'><img src="https://github.com/LCO-Embedding/LCO-Embedding/raw/main/assets/scaling.png" alt="scaling_law" width="100%"/></div>
+
+## Citation
+
+If you find LCO-Embedding useful for your research and applications, please cite using this BibTeX:
+
+```bibtex
+@misc{xiao2025scaling,
+    title={Scaling Language-Centric Omnimodal Representation Learning}, 
+    author={Chenghao Xiao and Hou Pong Chan and Hao Zhang and Weiwen Xu and Mahani Aljunied and Yu Rong},
+    year={2025},
+    eprint={2510.11693},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/2510.11693}, 
 }
 ```