---
title: Alibaba-NLP/gte-reranker-modernbert-base
description: We are excited to introduce the `gte-modernbert` series of models, which are built upon the latest modernBERT pre-trained encoder-only found. ModernBERT, 150M parameters.
canonical_url: https://superlinked.com/models/alibaba-nlp-gte-reranker-modernbert-base
last_updated: 2026-09-05
---

# Alibaba-NLP/gte-reranker-modernbert-base

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.

Source: [Alibaba-NLP/gte-reranker-modernbert-base on HuggingFace](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base)
Base model: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)

## Overview

| Field | Value |
|-------|-------|
| Architecture | ModernBERT |
| Parameters | 150M |
| Tasks | Score |
| Outputs | Score |
| Max sequence length | 8,192 tokens |
| License | apache-2.0 |
| Inputs | text |
| Languages | en |

## Benchmarks

### AskUbuntuDupQuestions

Domain: technology · Task: reranking · Language: 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](https://github.com/taolei87/askubuntu)

### CMedQAv1Reranking

Domain: medical · Task: reranking · Language: 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](https://github.com/zhangsheng93/cMedQA)

### CMedQAv2Reranking

Domain: medical · Task: reranking · Language: 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](https://github.com/zhangsheng93/cMedQA2)

### CQADupstackPhysicsRetrieval

Domain: scientific · Task: retrieval · Language: 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](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)

### CosQA

Domain: technology · Task: retrieval · Language: 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](https://arxiv.org/abs/2105.13239)

### FiQA2018

Domain: finance · Task: retrieval · Language: 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](https://sites.google.com/view/fiqa/)

### LegalBenchConsumerContractsQA

Domain: legal · Task: retrieval · Language: 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](https://huggingface.co/datasets/nguha/legalbench)

### MMarcoReranking

Domain: general · Task: reranking · Language: zh

Multilingual MARCO passage reranking (Chinese)

**Quality:** map at 10: 0.2271 · mrr at 10: 0.2373

[Reference](https://arxiv.org/abs/2304.03679)

### NFCorpus

Domain: medical · Task: retrieval · Language: 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](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)

### SCIDOCS

Domain: scientific · Task: retrieval · Language: 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](https://allenai.org/data/scidocs)

### SciFact

Domain: scientific · Task: retrieval · Language: 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](https://github.com/allenai/scifact)

### StackOverflowQA

Domain: technology · Task: retrieval · Language: 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](https://arxiv.org/abs/2407.02883)

### T2Reranking

Domain: general · Task: reranking · Language: zh

Chinese passage ranking benchmark

**Quality:** map at 10: 0.5537 · mrr at 10: 0.7882

[Reference](https://arxiv.org/abs/2304.03679)