Update README.md

README.md

@@ -2604,9 +2604,9 @@ language:
 license: mit
 ---
 
-# E5-base-v2
+# E5-base-4k
 
-[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.
 
@@ -2664,7 +2664,7 @@ 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). Note that E5-Base-4k simply expands the position embedding matrix to allow for 4,096 position ids. The embedding vectors for the original pids {0,1,2,...,511} is mapped to represent {0,8,16,...,4088}. Embedding vectors for other pids are trained. So for inputs not exceeding 512 tokens, please multiply the position ids by 8 to maintain the original behavior, as shown in the code above.
+Please refer to our paper at [https://arxiv.org/abs/2404.12096.pdf](https://arxiv.org/abs/2404.12096.pdf). Note that E5-Base-4k simply expands the position embedding matrix to allow for 4,096 position ids. The embedding vectors for the original pids {0,1,2,...,511} is mapped to represent {0,8,16,...,4088}. Embedding vectors for other pids are trained. So for inputs not exceeding 512 tokens, please multiply the position ids by 8 to maintain the original behavior, as shown in the code above.
 
 ## Benchmark Evaluation
 
@@ -2676,10 +2676,10 @@ on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arx
 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}
 }
 ```