---
title: cross-encoder/ms-marco-MiniLM-L-6-v2
description: This model was trained on the MS Marco Passage Ranking task.. BERT, 23M parameters.
canonical_url: https://superlinked.com/models/cross-encoder-ms-marco-minilm-l-6-v2
last_updated: 2026-06-04
---

# cross-encoder/ms-marco-MiniLM-L-6-v2

This model was trained on the MS Marco Passage Ranking task.

Source: [cross-encoder/ms-marco-MiniLM-L-6-v2 on HuggingFace](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2)
Base model: [cross-encoder/ms-marco-MiniLM-L12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L12-v2)

## Overview

| Field | Value |
|-------|-------|
| Architecture | BERT |
| Parameters | 23M |
| Tasks | Score |
| Outputs | Score |
| Max sequence length | 512 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: 0.6027 · map at 10: 0.4439 · mrr at 10: 0.6776

**Performance (L4 b1 c16):** Query 827 tok/s · Query p50 411.2ms

[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: 0.0835 · mrr at 10: 0.1371

[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: 0.0926 · mrr at 10: 0.1425

[Reference](https://github.com/zhangsheng93/cMedQA2)

### CQADupstackPhysicsRetrieval?candidates_model=Alibaba-NLP

Domain: general · Task: retrieval · Language: en

**Performance (L4 b1 c16):** Query 44.3K tok/s · Query p50 44.6ms

### CosQA?candidates_model=Alibaba-NLP

Domain: general · Task: retrieval · Language: en

**Performance (L4 b1 c16):** Query 20.5K tok/s · Query p50 43.6ms

### FiQA2018?candidates_model=Alibaba-NLP

Domain: general · Task: retrieval · Language: en

**Performance (L4 b1 c16):** Query 51.1K tok/s · Query p50 43.4ms

### LegalBenchConsumerContractsQA?candidates_model=Alibaba-NLP

Domain: general · Task: retrieval · Language: en

**Performance (L4 b1 c16):** Query 91.7K tok/s · Query p50 45.6ms

### MMarcoReranking

Domain: general · Task: reranking · Language: zh

Multilingual MARCO passage reranking (Chinese)

**Quality:** map at 10: 0.0543 · mrr at 10: 0.0544

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

### NFCorpus?candidates_model=Alibaba-NLP

Domain: general · Task: retrieval · Language: en

**Performance (L4 b1 c16):** Query 70.8K tok/s · Query p50 45.9ms

### NanoFiQA2018Retrieval

Domain: finance · Task: retrieval · Language: en

Smaller subset of the FiQA financial QA dataset

**Performance (L4 b1 c16):** Query 7.5K tok/s · Query p50 388.1ms

[Reference](https://sites.google.com/view/fiqa/)

### SCIDOCS?candidates_model=Alibaba-NLP

Domain: general · Task: retrieval · Language: en

**Performance (L4 b1 c16):** Query 53.7K tok/s · Query p50 42.5ms

### SciFact?candidates_model=Alibaba-NLP

Domain: general · Task: retrieval · Language: en

**Performance (L4 b1 c16):** Query 67.4K tok/s · Query p50 42.1ms

### StackOverflowQA?candidates_model=Alibaba-NLP

Domain: general · Task: retrieval · Language: en

**Performance (L4 b1 c16):** Query 98.6K tok/s · Query p50 47.2ms

### T2Reranking

Domain: general · Task: reranking · Language: zh

Chinese passage ranking benchmark

**Quality:** map at 10: 0.4714 · mrr at 10: 0.7102

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