link to arxiv

README.md

@@ -8647,7 +8647,8 @@ model-index:
 
 Our new model that naturally integrates "context tokens" into the embedding process. As of October 1st, 2024, `cde-small-v1` is the best small model (under 400M params) on the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) for text embedding models, with an average score of 65.00.
 
-<b><a href="https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing">Try on Colab</a></b>
+👉  <b><a href="https://colab.research.google.com/drive/1r8xwbp7_ySL9lP-ve4XMJAHjidB9UkbL?usp=sharing">Try on Colab</a></b>
+👉  <b><a href="https://arxiv.org/abs/2410.02525">Contextual Document Embeddings (ArXiv)</a></b>
 
 ![CDE Overview Figure](https://i.imgur.com/LyXJZjM.png)
 
@@ -8759,3 +8760,18 @@ We've set up a short demo in a Colab notebook showing how you might use our mode
 ### Acknowledgments
 
 Early experiments on CDE were done with support from [Nomic](https://www.nomic.ai/) and [Hyperbolic](https://hyperbolic.xyz/). We're especially indebted to Nomic for [open-sourcing their efficient BERT implementation and contrastive pre-training data](https://arxiv.org/abs/2402.01613), which proved vital in the development of CDE.
+
+### Cite us
+
+Used our model, method, or architecture? Want to cite us? Here's the ArXiv citation information:
+```
+@misc{morris2024contextualdocumentembeddings,
+      title={Contextual Document Embeddings}, 
+      author={John X. Morris and Alexander M. Rush},
+      year={2024},
+      eprint={2410.02525},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.02525}, 
+}
+```