Update README.md

README.md

@@ -13,7 +13,7 @@ metrics:
 ---
 
 # Naive Listwise MonoBERT trained on Baidu-ULTR
-A flax-based MonoBERT cross encoder trained on the [Baidu-ULTR](https://arxiv.org/abs/2207.03051) dataset with a **listwise softmax cross-entropy loss on clicks**. The loss is called "naive" as we use user clicks as a signal of relevance without any additional position bias correction. For more info, [read our paper here](https://arxiv.org/abs/2404.02543).
+A flax-based MonoBERT cross encoder trained on the [Baidu-ULTR](https://arxiv.org/abs/2207.03051) dataset with a **listwise softmax cross-entropy loss on clicks**. The loss is called "naive" as we use user clicks as a signal of relevance without any additional position bias correction. For more info, [read our paper](https://arxiv.org/abs/2404.02543) and [find the code for this model here](https://github.com/philipphager/baidu-bert-model).
 
 ## Test Results on Baidu-ULTR Expert Annotations
 
@@ -69,3 +69,14 @@ print(outputs)
 
 For more details on how to use the model with real data from Baidu-ULTR, take a look at the [evaluation script of our model repository](https://github.com/philipphager/baidu-bert-model/blob/main/eval.py).
 
+## Reference
+```
+@inproceedings{Hager2024BaiduULTR,
+  author = {Philipp Hager and Romain Deffayet and Jean-Michel Renders and Onno Zoeter and Maarten de Rijke},
+  title = {Unbiased Learning to Rank Meets Reality: Lessons from Baidu’s Large-Scale Search Dataset},
+  booktitle = {Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR`24)},
+  organization = {ACM},
+  year = {2024},
+}
+```
+