Update README.md

README.md

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-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
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+## CellCLIP - Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+CellCLIP, a cross-modal contrastive learning framework for HCS data. CellCLIP leverages pre-trained image encoders coupled with a novel channel encoding scheme to better capture relationships between different microscopy channels in image embeddings, along with natural language encoders for repre senting perturbations. Our framework outperforms current open-source models, demonstrating the best performance in both cross-modal retrieval and biologically meaningful downstream tasks while also achieving significant reductions in computation time. 
+
+* [Paper](https://arxiv.org/pdf/2506.06290)
+* [Github](https://github.com/suinleelab/CellCLIP/tree/main)
+
+## Citation
+```
+@article{lu2025cellclip,
+  title={CellCLIP--Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning},
+  author={Lu, Mingyu and Weinberger, Ethan and Kim, Chanwoo and Lee, Su-In},
+  journal={arXiv preprint arXiv:2506.06290},
+  year={2025}
+}
+```