Update README.md

README.md

@@ -16,17 +16,9 @@ In this project, we introduce BGE-M3, which is distinguished for its versatility
 - Multi-Linguality: It can support more than 100 working languages. 
 - Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens. 
 
-**Some suggestions for retrieval pipeline in RAG:**
-We recommend to use following pipeline: hybrid retrieval + re-ranking. 
-- Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. 
-A classic example: using both embedding retrieval and the BM25 algorithm. 
-Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval. 
-This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings.
-- As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model. 
-Utilizing the re-ranking model (e.g., [bge-reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker), [cohere-reranker](https://txt.cohere.com/rerank/)) after retrieval can further filter the selected text.
-
 
 ## News:
+- 2024/3/8: **Thanks for the [experimental results](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) from @[Yannael](https://huggingface.co/Yannael). In this benchmark, BGE-M3 achieves top performance in both English and other languages, surpassing models such as OpenAI.**
 - 2024/3/2: Release unified fine-tuning [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune) and [data](https://huggingface.co/datasets/Shitao/bge-m3-data) 
 - 2024/2/6: 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). 
 - 2024/2/1: **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)
@@ -95,6 +87,15 @@ If you want to fine-tune all embedding function of m3, you can refer to the [uni
 
 
 
+**5. Some suggestions for retrieval pipeline in RAG**
+We recommend to use following pipeline: hybrid retrieval + re-ranking. 
+- Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. 
+A classic example: using both embedding retrieval and the BM25 algorithm. 
+Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval. 
+This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings.
+- As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model. 
+Utilizing the re-ranking model (e.g., [bge-reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker), [cohere-reranker](https://txt.cohere.com/rerank/)) after retrieval can further filter the selected text.
+
 
 
 ## Usage
@@ -217,9 +218,13 @@ print(model.compute_score(sentence_pairs,
 
 ## Evaluation  
 
+### Benchmarks from the open-source community
+  ![avatar](./imgs/others.webp)
+ The BGE-M3 model emerged as the top performer on this benchmark (OAI is short for OpenAI). 
+  For more details, please refer to the [article](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) and [Github Repo](https://github.com/Yannael/multilingual-embeddings)
 
-We compare BGE-M3 with some popular methods, including BM25, openAI embedding, etc.
 
+### Our results
 - Multilingual (Miracl dataset) 
 
 ![avatar](./imgs/miracl.jpg)
@@ -252,6 +257,7 @@ especially in long document retrieval.
 ![avatar](./imgs/bm25.jpg)
 
 
+
 ## Training
 - Self-knowledge Distillation: combining multiple outputs from different 
 retrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)
@@ -266,8 +272,8 @@ Refer to our [report](https://arxiv.org/pdf/2402.03216.pdf) for more details.
 
 ## Acknowledgement
 
-Thanks the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc. 
-Thanks the open-sourced libraries like [Tevatron](https://github.com/texttron/tevatron), [Pyserini](https://github.com/castorini/pyserini).
+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).
 
 
 
@@ -284,4 +290,4 @@ If you find this repository useful, please consider giving a star :star: and cit
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```
+```