Model reference · open weights
bge-m3 is an open-weight embedding model from BAAI. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Maker | BAAI |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Context | 8194 tokens |
| Runs with | sentence-transformers |
| Released | 2024-01-27 |
| Popularity | 37.1M downloads / month |
| Licence | Open weights |
About
For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding
In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
Some suggestions for retrieval pipeline in RAG
We recommend to use the 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. To use hybrid retrieval, you can refer to Vespa and Milvus.
As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model. Utilizing the re-ranking model (e.g., bge-reranker, bge-reranker-v2) after retrieval can further filter the selected text.
2024/7/1: We update the MIRACL evaluation results of BGE-M3. To reproduce the new results, you can refer to: bge-m3_miracl_2cr. We have also updated our paper on arXiv.
The previous test results were lower because we mistakenly removed the passages that have the same id as the query from the search results. After correcting this mistake, the overall performance of BGE-M3 on MIRACL is higher than the previous results, but the experimental conclusion remains unchanged. The other results are not affected by this mistake. To reproduce the previous lower results, you need to add the --remove-query parameter when using pyserini.search.faiss or pyserini.search.lucene to search the passages.
2024/3/20: Thanks Milvus team! Now you can use hybrid retrieval of bge-m3 in Milvus: pymilvus/examples /hello_hybrid_sparse_dense.py.
2024/3/8: Thanks for the experimental results from @Yannael. In this benchmark, BGE-M3 achieves top performance in both English and other languages, surpassing models such as OpenAI.
2024/2/6: We release the MLDR (a long document retrieval dataset covering 13 languages) and evaluation pipeline.
2024/2/1: Thanks for the excellent tool from Vespa. You can easily use multiple modes of BGE-M3 following this notebook
| Model Name | Dimension | Sequence Length | Introduction |
|---|---|---|---|
| BAAI/bge-m3 | 1024 | 8192 | multilingual; unified fine-tuning (dense, sparse, and colbert) from bge-m3-unsupervised |
| BAAI/bge-m3-unsupervised | 1024 | 8192 | multilingual; contrastive learning from bge-m3-retromae |
| BAAI/bge-m3-retromae | -- | 8192 | multilingual; extend the max_length of xlm-roberta to 8192 and further pretrained via retromae |
| BAAI/bge-large-en-v1.5 | 1024 | 512 | English model |
| BAAI/bge-base-en-v1.5 | 768 | 512 | English model |
| BAAI/bge-small-en-v1.5 | 384 | 512 | English model |
| Dataset | Introduction |
|---|---|
| MLDR | Docuemtn Retrieval Dataset, covering 13 languages |
| bge-m3-data | Fine-tuning data used by bge-m3 |
1. Introduction for different retrieval methods
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys bge-m3 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (bge-m3 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"bge-m3","input":"text to embed"}'
Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.