metadata
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)
from FlagEmbedding import FlagReranker
reranker = FlagReranker("BAAI/bge-reranker-base", use_fp16=True)
score = reranker.compute_score(["query", "passage"])
print(score)