Update README.md

README.md

@@ -11,7 +11,7 @@ license: mit
 
 # E5-RoPE-Base
 
-[LongEmbed: Extending Embedding Models for Long Context Retrieval](). Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li, arxiv 2024. Github Repo for LongEmbed: https://github.com/dwzhu-pku/LongEmbed.
+[LongEmbed: Extending Embedding Models for Long Context Retrieval](https://arxiv.org/abs/2404.12096). Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li, arxiv 2024. Github Repo for LongEmbed: https://github.com/dwzhu-pku/LongEmbed.
 
 This model has 12 layers and the embedding size is 768.
 
@@ -47,7 +47,7 @@ print(scores.tolist())
 
 ## Training Details
 
-Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
+Please refer to our paper at [https://arxiv.org/abs/2404.12096.pdf](https://arxiv.org/abs/2404.12096.pdf).
 
 ## Benchmark Evaluation
 
@@ -55,7 +55,7 @@ Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to repro
 on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
 
 
-Please note that E5-RoPE-Base is not specifically trained for optimized performance. Its purpose is to enable performance comparisons between embedding models that utilize absolute position embeddings (APE) and rotary position embeddings (RoPE). By comparing E5-Base and E5-RoPE-Base, we demonstrate the superiority of RoPE-based embedding models in effectively managing longer context. See our paper [LongEmbed: Extending Embedding Models for Long Context Retrieval]() for more details.
+Please note that E5-RoPE-Base is not specifically trained for optimized performance. Its purpose is to enable performance comparisons between embedding models that utilize absolute position embeddings (APE) and rotary position embeddings (RoPE). By comparing E5-Base and E5-RoPE-Base, we demonstrate the superiority of RoPE-based embedding models in effectively managing longer context. See our paper [LongEmbed: Extending Embedding Models for Long Context Retrieval](https://arxiv.org/abs/2404.12096) for more details.
 
 
 ## Citation
@@ -63,10 +63,10 @@ Please note that E5-RoPE-Base is not specifically trained for optimized performa
 If you find our paper or models helpful, please consider cite as follows:
 
 ```
-@article{wang2022text,
-  title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
-  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
-  journal={arXiv preprint arXiv:2212.03533},
-  year={2022}
+@article{zhu2024longembed,
+  title={LongEmbed: Extending Embedding Models for Long Context Retrieval},
+  author={Zhu, Dawei and Wang, Liang and Yang, Nan and Song, Yifan and Wu, Wenhao and Wei, Furu and Li, Sujian},
+  journal={arXiv preprint arXiv:2404.12096},
+  year={2024}
 }
 ```