From the model card
Fields below are copied from the tags and counters on the HuggingFace repository BAAI/bge-m3 at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- BAAI
- Pipeline tag
- sentence-similarity
- Library
- Sentence Transformers
- Framework tags
- PyTorch
- Weight formats
- ONNX
- License tag
mit— read the license file in the repo before relying on it- Papers cited
- arXiv:2402.03216, arXiv:2004.04906, arXiv:2106.14807, arXiv:2107.05720, arXiv:2004.12832
- Downloads (HF counter at last fetch)
- 36,725,443
- Likes (HF counter at last fetch)
- 3,466
- Model card
- https://huggingface.co/BAAI/bge-m3
Use cases
- Multilingual semantic search across 100+ language corpora
- Cross-lingual retrieval for international knowledge bases and documentation
- Hybrid dense+sparse retrieval combining semantic and keyword matching signals
- Dense passage retrieval in RAG pipelines serving non-English content
- Large-scale multilingual document indexing
Pros
- 100+ language coverage eliminates per-language model management overhead
- Unified dense/sparse/ColBERT outputs enable flexible retrieval strategies
- MIT license; strong MTEB multilingual leaderboard performance
- XLM-RoBERTa backbone brings established multilingual pretraining quality
Cons
- Larger than smaller BGE variants, increasing deployment memory requirements
- Dense + sparse + ColBERT inference modes add compute overhead over single-mode bi-encoders
- Quality gaps between high-resource and low-resource language coverage
- Complex deployment compared to standard single-mode embedding models
- ONNX export may not cover all retrieval modes
Tags
sentence-transformerspytorchonnxxlm-robertafeature-extractionsentence-similarityarxiv:2402.03216arxiv:2004.04906arxiv:2106.14807arxiv:2107.05720arxiv:2004.12832license:miteval-resultstext-embeddings-inferenceendpoints_compatibleregion:usdeploy:sagemakerdeploy:azure