BAAI/bge-m3
Based on XLM-RoBERTa with 8,192 token context length supporting 100+ languages with multi-functionality. Uniquely performs dense, multi-vector, and sparse retrieval simultaneously achieving SOTA on MIRACL and MKQA benchmarks. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- BAAI
- License
- Open Source
- Price per 1M tokens
- $0.010
- Dimensions
- 1024
- Release Date
- 2024-01-27
- Model Name
- bge-m3
- Total Evaluations
- 830
Performance Record
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Performance Overview
ELO ratings by dataset
BAAI/bge-m3's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
BAAI/bge-m3 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 27ms
- P50 (Median)
- 27ms
- P90
- 27ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.801
- nDCG@10
- 0.785
- Recall@5
- 0.061
- Recall@10
- 0.122
Latency Distribution
- Mean
- 21ms
- P50 (Median)
- 21ms
- P90
- 21ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.743
- nDCG@10
- 0.755
- Recall@5
- 0.608
- Recall@10
- 0.667
Latency Distribution
- Mean
- 22ms
- P50 (Median)
- 22ms
- P90
- 22ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.571
- nDCG@10
- 0.599
- Recall@5
- 0.645
- Recall@10
- 0.759
Latency Distribution
- Mean
- 37ms
- P50 (Median)
- 37ms
- P90
- 37ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.956
- nDCG@10
- 0.941
- Recall@5
- 0.121
- Recall@10
- 0.219
Latency Distribution
- Mean
- 51ms
- P50 (Median)
- 51ms
- P90
- 51ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.879
- nDCG@10
- 0.879
- Recall@5
- 0.960
- Recall@10
- 0.960
Latency Distribution
- Mean
- 48ms
- P50 (Median)
- 48ms
- P90
- 48ms
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked embedding models and smart retrieval 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-m3 with other top embeddings to understand the differences in performance, accuracy, and latency.