| --- |
| license: mit |
| language: |
| - en |
| - zh |
| tags: |
| - mteb |
| model-index: |
| - name: bge-reranker-base |
| results: |
| - task: |
| type: Reranking |
| dataset: |
| type: C-MTEB/CMedQAv1-reranking |
| name: MTEB CMedQAv1 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map |
| value: 81.27206722525007 |
| - type: mrr |
| value: 84.14238095238095 |
| - task: |
| type: Reranking |
| dataset: |
| type: C-MTEB/CMedQAv2-reranking |
| name: MTEB CMedQAv2 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map |
| value: 84.10369934291236 |
| - type: mrr |
| value: 86.79376984126984 |
| - task: |
| type: Reranking |
| dataset: |
| type: C-MTEB/Mmarco-reranking |
| name: MTEB MMarcoReranking |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map |
| value: 35.4600511272538 |
| - type: mrr |
| value: 34.60238095238095 |
| - task: |
| type: Reranking |
| dataset: |
| type: C-MTEB/T2Reranking |
| name: MTEB T2Reranking |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map |
| value: 67.27728847727172 |
| - type: mrr |
| value: 77.1315192743764 |
|
|
| pipeline_tag: text-classification |
| --- |
| |
| # 🔍 BGE Reranker Base (Fine-tuned) |
|
|
| This repository contains a **fine-tuned cross-encoder reranker model** based on `BAAI/bge-reranker-base`. |
|
|
| It is designed to improve retrieval pipelines by re-ranking top-K results from embedding models. |
|
|
| --- |
|
|
| ## 🚀 Key Features |
|
|
| - Cross-encoder reranking (query + passage scoring) |
| - Strong performance on MTEB / C-MTEB benchmarks |
| - Supports multilingual (English + Chinese) |
| - Optimized for semantic search pipelines |
|
|
| --- |
|
|
| ## 📦 Model Files |
|
|
| This repo contains: |
|
|
| ✔ config.json |
| ✔ model.safetensors |
| ✔ tokenizer_config.json |
| ✔ sentencepiece.bpe.model |
| ✔ special_tokens_map.json (fixed naming) |
| ✔ README.md |
| |
| |
| ## ⚙️ Usage |
| |
| ### 🔹 Using FlagEmbedding (Recommended) |
| |
| ```python |
| from FlagEmbedding import FlagReranker |
| |
| reranker = FlagReranker("BAAI/bge-reranker-base", use_fp16=True) |
|
|
| score = reranker.compute_score(["query", "passage"]) |
| print(score) |
