bge-reranker

Open weights · BAAI · free to self-host

The bge-reranker family from BAAI (Beijing Academy of Artificial Intelligence) are open-weight cross-encoders trained specifically for passage reranking. They're the standard self-hosted option: free to download, drop into sentence-transformers, and strong enough to beat most commercial APIs on English benchmarks.

Model variants

Recommendation: start with bge-reranker-v2-m3. It's multilingual, has strong BEIR numbers, and works fine on CPU for moderate traffic. Upgrade to the Gemma variant only if you have GPU and need maximum quality.

Benchmarks

Scores are approximate averages across the 18 BEIR datasets. Results vary by dataset — always evaluate on your own domain.

Quick start

Install

pip install sentence-transformers

Rerank a list of passages

from sentence_transformers import CrossEncoder

model = CrossEncoder("BAAI/bge-reranker-v2-m3", max_length=512)

query = "How do I add reranking to my RAG pipeline?"
passages = [
    "Rerankers score each query-passage pair with a cross-encoder.",
    "BM25 is a classical keyword-based retrieval method.",
    "London is the capital of the United Kingdom.",
    "Two-stage retrieval: retrieve 50 candidates, rerank to top 5.",
]

scores = model.predict([(query, p) for p in passages])
ranked = sorted(zip(scores, passages), reverse=True)

for score, text in ranked:
    print(f"{score:.4f}  {text[:80]}")

In a RAG pipeline

from sentence_transformers import CrossEncoder

reranker = CrossEncoder("BAAI/bge-reranker-v2-m3")

def rag_answer(query: str, vector_db, llm) -> str:
    # Stage 1: retrieve wide
    candidates = vector_db.search(query, top_k=50)
    # Stage 2: rerank tight
    scores = reranker.predict([(query, c) for c in candidates])
    top5 = [c for _, c in sorted(zip(scores, candidates), reverse=True)[:5]]
    # Stage 3: generate
    return llm.complete(f"Context:\n" + "\n\n".join(top5) + f"\n\nQ: {query}")

Pros and cons

Other models