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BAAI/bge-reranker-v2-m3

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Primitive: /score · Score · XLM-RoBERTa

More details please refer to our Github: FlagEmbedding.

MultilingualLong context

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Overview

Hardware: — drives latency, throughput & cost

Size568M params
Tasks /score
Licenseapache-2.0
Languagesmultilingual
Latency92 ms
Throughput30.0K tok/s
Cost$0.0074 /1M tok

Cost is approximate — computed from list GPU prices; your actual price depends on the provider you deploy SIE with.

Scoring

Inputstext
Max sequence length8,192

Benchmarks

AskUbuntuDupQuestions

technology reranking en

Duplicate question detection from AskUbuntu

Corpus: 6,743 Queries: 360
Quality
ndcg at 10 0.6763
map at 10 0.5245
mrr at 10 0.7611
Performance L4 b1 c16
Query 6.5K tok/s
Query p50 40.6ms
Reference →

CMedQAv1Reranking

medical reranking zh

Chinese medical question answering reranking (v1)

Corpus: 100,000 Queries: 2,000
Quality
map at 10 0.8192
mrr at 10 0.8550
Reference →

CMedQAv2Reranking

medical reranking zh

Chinese medical question answering reranking (v2)

Corpus: 108,000 Queries: 4,000
Quality
map at 10 0.8287
mrr at 10 0.8608
Reference →

CQADupstackPhysicsRetrieval

scientific retrieval en

Duplicate question retrieval from StackExchange Physics

Corpus: 38,314 Queries: 1,039
Quality
map at 10 0.3618
mrr at 10 0.4262
ndcg at 10 0.4167
Reference →

CQADupstackPhysicsRetrieval (candidates: gte-multilingual-base, k=50)

scientific retrieval en

Duplicate question retrieval from StackExchange Physics

Performance L4 b1 c16
Query 26.5K tok/s
Query p50 89.8ms
Reference →

CosQA

technology retrieval en

Code search with natural language queries

Corpus: 6,267 Queries: 500
Quality
map at 10 0.2169
mrr at 10 0.2331
ndcg at 10 0.2897
Reference →

CosQA (candidates: gte-multilingual-base, k=50)

technology retrieval en

Code search with natural language queries

Performance L4 b1 c16
Query 13.9K tok/s
Query p50 66.1ms
Reference →

FiQA2018

finance retrieval en

Financial opinion mining and question answering

Corpus: 57,599 Queries: 648
Quality
map at 10 0.3675
mrr at 10 0.5479
ndcg at 10 0.4504
Reference →

FiQA2018 (candidates: gte-multilingual-base, k=50)

finance retrieval en

Financial opinion mining and question answering

Performance L4 b1 c16
Query 30.0K tok/s
Query p50 90.2ms
Reference →

LegalBenchConsumerContractsQA

legal retrieval en

Question answering on consumer contracts

Corpus: 153 Queries: 396
Quality
map at 10 0.7734
mrr at 10 0.7757
ndcg at 10 0.8156
Reference →

LegalBenchConsumerContractsQA (candidates: gte-multilingual-base, k=50)

legal retrieval en

Question answering on consumer contracts

Performance L4 b1 c16
Query 51.3K tok/s
Query p50 93.5ms
Reference →

MMarcoReranking

general reranking zh

Multilingual MARCO passage reranking (Chinese)

Quality
map at 10 0.3354
mrr at 10 0.3401
Performance L4 b1 c16
Reference →

NFCorpus

medical retrieval en

Biomedical literature search from NutritionFacts.org

Corpus: 3,593 Queries: 323
default_bm25-k-100
Quality
ndcg at 10 0.3285
map at 10 0.2339
mrr at 10 0.5301
default_bm25-k-100_qrels-k-50
Quality
ndcg at 10 0.3930
map at 10 0.2792
mrr at 10 0.5996
default_bm25-k-50_qrels-k-50
Quality
ndcg at 10 0.4243
map at 10 0.3065
mrr at 10 0.6220
default_bm25-k-50_qrels-k-10
Quality
ndcg at 10 0.4029
map at 10 0.2832
mrr at 10 0.6087
default_bm25-k-100_qrels-k-10
Quality
ndcg at 10 0.3759
map at 10 0.2621
mrr at 10 0.5876
default_bm25-k-50
Quality
ndcg at 10 0.3306
map at 10 0.2375
mrr at 10 0.5380
default_bm25-k-200_qrels-k-50
Quality
ndcg at 10 0.3674
map at 10 0.2602
mrr at 10 0.5765
default_candidates-k-50_candidates-model-Alibaba-NLP__gte-multilingual-base
Quality
map at 10 0.2510
mrr at 10 0.5638
ndcg at 10 0.3530
default_bm25-k-200
Quality
ndcg at 10 0.3280
map at 10 0.2331
mrr at 10 0.5264
default_bm25-k-200_qrels-k-10
Quality
ndcg at 10 0.3557
map at 10 0.2497
mrr at 10 0.5677
Reference →

NFCorpus (candidates: gte-multilingual-base, k=50)

medical retrieval en

Biomedical literature search from NutritionFacts.org

Performance L4 b1 c16
Query 35.3K tok/s
Query p50 109.6ms
Reference →

SCIDOCS

scientific retrieval en

Citation prediction, document classification, and recommendation for scientific papers

Corpus: 25,656 Queries: 1,000
Quality
map at 10 0.1076
mrr at 10 0.3207
ndcg at 10 0.1871
Reference →

SCIDOCS (candidates: gte-multilingual-base, k=50)

scientific retrieval en

Citation prediction, document classification, and recommendation for scientific papers

Performance L4 b1 c16
Query 29.4K tok/s
Query p50 98.7ms
Reference →

SciFact

scientific retrieval en

Scientific claim verification using research literature

Corpus: 5,183 Queries: 300
Quality
map at 10 0.7013
mrr at 10 0.7184
ndcg at 10 0.7456
Reference →

SciFact (candidates: gte-multilingual-base, k=50)

scientific retrieval en

Scientific claim verification using research literature

Performance L4 b1 c16
Query 33.4K tok/s
Query p50 113.2ms
Reference →

StackOverflowQA

technology retrieval en

Programming question answering from Stack Overflow

Corpus: 19,931 Queries: 1,994
Quality
map at 10 0.5545
mrr at 10 0.5631
ndcg at 10 0.6158
Reference →

StackOverflowQA (candidates: gte-multilingual-base, k=50)

technology retrieval en

Programming question answering from Stack Overflow

Performance L4 b1 c16
Query 53.1K tok/s
Query p50 111.1ms
Reference →

T2Reranking

general reranking zh

Chinese passage ranking benchmark

Quality
map at 10 0.5630
mrr at 10 0.7781
Reference →

Open source inference for agents

Open-source inference for the models behind your agents. Run it yourself, or let us run it for you.

Github 3.3K

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