BAAI/BGE Reranker v2 M3
Lightweight 0.6B parameter cross-encoder built on bge-m3 foundation with LoRA fine-tuning and flash attention optimization. Strong multilingual support with fast inference, trained on diverse datasets including FEVER and MIRACL for production deployment efficiency. If you want to compare the best rerankers for your data, try Agentset.
Model Information
- Provider
- BAAI
- License
- Open Source
- Price per 1M tokens
- $0.020
- Release Date
- 2023-09-15
- Model Name
- bge-reranker-v2-m3
- Total Evaluations
- 3300
Performance Record
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Performance Overview
ELO ratings by dataset
BAAI/BGE Reranker v2 M3's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
BAAI/BGE Reranker v2 M3 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 2207ms
- P50 (Median)
- 825ms
- P90
- 1247ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 2451ms
- P50 (Median)
- 895ms
- P90
- 1679ms
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 2034ms
- P50 (Median)
- 1225ms
- P90
- 2091ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 2087ms
- P50 (Median)
- 806ms
- P90
- 1068ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.112
- nDCG@10
- 0.120
- Recall@5
- 0.105
- Recall@10
- 0.130
Latency Distribution
- Mean
- 2529ms
- P50 (Median)
- 1019ms
- P90
- 1649ms
arguana
Accuracy Metrics
- nDCG@5
- 0.312
- nDCG@10
- 0.386
- Recall@5
- 0.560
- Recall@10
- 0.780
Latency Distribution
- Mean
- 2989ms
- P50 (Median)
- 1658ms
- P90
- 2279ms
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked rerankers and embedding models built in. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Compare Models
See how it stacks up
Compare BAAI/BGE Reranker v2 M3 with other top rerankers to understand the differences in performance, accuracy, and latency.