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bge-reranker-v2-m3

BGE-Reranker-v2-M3 is BAAI's multilingual cross-encoder reranker built on XLM-RoBERTa, designed for re-ranking retrieved passages in multilingual RAG or search pipelines. It jointly encodes query-passage pairs to produce relevance scores, providing higher accuracy than bi-encoder similarity for the same candidate set. Apache 2.0 licensed with text-embeddings-inference support.

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From the model card

Fields below are copied from the tags and counters on the HuggingFace repository BAAI/bge-reranker-v2-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
text-classification
Library
Sentence Transformers, Transformers
Weight formats
safetensors
License tag
apache-2.0 — read the license file in the repo before relying on it
Language tags
multilingual
Papers cited
arXiv:2312.15503, arXiv:2402.03216
Downloads (HF counter at last fetch)
17,577,877
Likes (HF counter at last fetch)
1,161
Model card
https://huggingface.co/BAAI/bge-reranker-v2-m3

Use cases

  • Re-ranking multilingual retrieval results in RAG pipelines for higher precision
  • Cross-lingual passage ranking (query and passage in different languages)
  • Second-stage ranking in multilingual search systems
  • Relevance scoring for multilingual FAQ and document retrieval
  • Improving retrieval quality over BGE-M3 dense retrieval as a reranker pair

Pros

  • Multilingual support across 100+ languages from XLM-RoBERTa backbone
  • Apache 2.0 license; text-embeddings-inference compatible
  • Natural pairing with BGE-M3 as a two-stage retrieval system
  • Cross-encoder accuracy improvement over bi-encoder similarity for re-ranking

Cons

  • Re-ranking latency scales with candidate set size — impractical for large first-stage pools
  • Cannot index documents — must process each query-candidate pair
  • XLM-RoBERTa backbone quality gaps for low-resource languages
  • Slower than English-only cross-encoders for English-only pipelines
  • Accuracy improvement over simpler rerankers varies by domain and language

Tags

sentence-transformerssafetensorsxlm-robertatext-classificationtransformerstext-embeddings-inferencemultilingualarxiv:2312.15503arxiv:2402.03216license:apache-2.0endpoints_compatibledeploy:azureregion:us