Update README.md

README.md

@@ -27,7 +27,7 @@ Utilizing the re-ranking model (e.g., [bge-reranker](https://github.com/FlagOpen
 
 
 ## News:
-- 2/6/2024: We release the [MLDR](https://huggingface.co/datasets/Shitao/MLDR), a long document retrieval dataset covering 13 languages. 
+- 2/6/2024: We release the [MLDR](https://huggingface.co/datasets/Shitao/MLDR) (a long document retrieval dataset covering 13 languages) and [evaluation pipeline](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR). 
 - 2/1/2024: **Thanks for the excellent tool from Vespa.** You can easily use multiple modes of BGE-M3 following this [notebook](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb)
 
 
@@ -243,22 +243,29 @@ The small-batch strategy is simple but effective, which also can used to fine-tu
 - MCLS: A simple method to improve the performance on long text without fine-tuning. 
 If you have no enough resource to fine-tuning model with long text, the method is useful.
 
-Refer to our [report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) for more details.
+Refer to our [report](https://arxiv.org/pdf/2402.03216.pdf) for more details.
 
 **The fine-tuning codes and datasets will be open-sourced in the near future.**
 
 
-
 ## Acknowledgement
 
-Thanks to the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc.  
-Thanks to the open-sourced libraries like [Tevatron](https://github.com/texttron/tevatron), [pyserini](https://github.com/castorini/pyserini).
+Thanks the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc. 
+Thanks the open-sourced libraries like [Tevatron](https://github.com/texttron/tevatron), [pyserial](https://github.com/pyserial/pyserial).
+
 
 
 ## Citation
 
-If you find this repository useful, please consider giving a star :star: and a citation
+If you find this repository useful, please consider giving a star :star: and citation
 
 ```
-
+@misc{bge-m3,
+      title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, 
+      author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
+      year={2024},
+      eprint={2402.03216},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
 ```