Add task category and link to paper and GitHub repository

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the dataset card by:
- Adding the `image-classification` task category to the metadata.
- Linking the dataset to its official paper: [Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts](https://huggingface.co/papers/2602.03473).
- Providing a link to the official GitHub repository.
- Adding a brief description of the dataset's purpose for Class-Incremental Learning (CIL).

README.md

@@ -1,12 +1,27 @@
 ---
 license: apache-2.0
+task_categories:
+- image-classification
 ---
 
-- Before using the dataset, please visit: https://github.com/LMMMEng/CaRE.
+# OmniBenchmark-1K
 
-- If you find this dataset useful for your research, please cite:
+OmniBenchmark-1K is a challenging benchmark for Class-Incremental Learning (CIL) designed to evaluate performance on very long task sequences, ranging from 100 to over 300 non-overlapping tasks. 
 
-```
+The dataset was introduced in the paper [Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts](https://huggingface.co/papers/2602.03473).
+
+- **GitHub:** [https://github.com/LMMMEng/CaRE](https://github.com/LMMMEng/CaRE)
+- **Paper:** [https://huggingface.co/papers/2602.03473](https://huggingface.co/papers/2602.03473)
+
+## Description
+
+OmniBenchmark-1K provides a large-scale evaluation protocol for comprehensively assessing CIL methods. While standard benchmarks often focus on 5-20 tasks, this dataset allows for performance evaluation on extremely long sequences, testing the stability and plasticity of models over time.
+
+## Citations
+
+If you find this dataset useful for your research, please cite:
+
+```bibtex
 @inproceedings{lou2026scaling,
   title={Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts},
   author={Lou, Meng and Fu, Yunxiang and Yu, Yizhou},