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Alibaba-NLP/gte-reranker-modernbert-base

Open comparison →

Primitive: /score · Score · ModernBERT

We are excited to introduce the `gte-modernbert` series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The `gte-modernbert` series models include both text embedding models and rerank models.

Long context

Overview

Hardware: — drives latency, throughput & cost

Size150M params
Tasks /score
Licenseapache-2.0
Languagesen
Latency55 ms
Throughput11.0K tok/s
Cost$0.020 /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 0.6701
map at 10 0.5148
mrr at 10 0.7570
Performance L4 b1 c16
Query 6.2K tok/s
Query p50 41.9ms
Reference →

CMedQAv1Reranking

medical reranking zh

Chinese medical question answering reranking (v1)

Corpus: 100,000 Queries: 2,000
Quality
map at 10 0.4989
mrr at 10 0.5905
Reference →

CMedQAv2Reranking

medical reranking zh

Chinese medical question answering reranking (v2)

Corpus: 108,000 Queries: 4,000
Quality
map at 10 0.5024
mrr at 10 0.5880
Reference →

CQADupstackPhysicsRetrieval

scientific retrieval en

Duplicate question retrieval from StackExchange Physics

Corpus: 38,314 Queries: 1,039
Quality
map at 10 0.4232
mrr at 10 0.4906
ndcg at 10 0.4795
Reference →

CosQA

technology retrieval en

Code search with natural language queries

Corpus: 6,267 Queries: 500
Quality
map at 10 0.2903
mrr at 10 0.3026
ndcg at 10 0.3744
Reference →

FiQA2018

finance retrieval en

Financial opinion mining and question answering

Corpus: 57,599 Queries: 648
Quality
map at 10 0.4075
mrr at 10 0.5683
ndcg at 10 0.4913
Reference →

LegalBenchConsumerContractsQA

legal retrieval en

Question answering on consumer contracts

Corpus: 153 Queries: 396
Quality
map at 10 0.8134
mrr at 10 0.8145
ndcg at 10 0.8491
Reference →

MMarcoReranking

general reranking zh

Multilingual MARCO passage reranking (Chinese)

Quality
map at 10 0.2271
mrr at 10 0.2373
Performance L4 b1 c16
Reference →

NFCorpus

medical retrieval en

Biomedical literature search from NutritionFacts.org

Corpus: 3,593 Queries: 323
Quality
map at 10 0.2702
mrr at 10 0.5846
ndcg at 10 0.3721
Reference →

SCIDOCS

scientific retrieval en

Citation prediction, document classification, and recommendation for scientific papers

Corpus: 25,656 Queries: 1,000
Quality
map at 10 0.1207
mrr at 10 0.3559
ndcg at 10 0.2041
Reference →

SciFact

scientific retrieval en

Scientific claim verification using research literature

Corpus: 5,183 Queries: 300
Quality
map at 10 0.7250
mrr at 10 0.7375
ndcg at 10 0.7653
Reference →

StackOverflowQA

technology retrieval en

Programming question answering from Stack Overflow

Corpus: 19,931 Queries: 1,994
Quality
map at 10 0.8382
mrr at 10 0.8399
ndcg at 10 0.8641
Reference →

T2Reranking

general reranking zh

Chinese passage ranking benchmark

Quality
map at 10 0.5537
mrr at 10 0.7882
Reference →

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