---
title: lightonai/GTE-ModernColBERT-v1 (Encode)
description: This is a PyLate model trained on the ms-marco-en-bge-gemma dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense ve. ModernBERT, 149M parameters.
canonical_url: https://superlinked.com/models/lightonai-gte-moderncolbert-v1--encode
last_updated: 2026-09-05
---

# lightonai/GTE-ModernColBERT-v1 (Encode)

This is a PyLate model trained on the ms-marco-en-bge-gemma dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Source: [lightonai/GTE-ModernColBERT-v1 on HuggingFace](https://huggingface.co/lightonai/GTE-ModernColBERT-v1)
Base model: [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base)

## Overview

| Field | Value |
|-------|-------|
| Architecture | ModernBERT |
| Parameters | 149M |
| Tasks | Encode, Score |
| Outputs | Multi-Vec |
| Dimensions | Multi-Vec: 128 |
| Max sequence length | 8,192 tokens |
| License | apache-2.0 |
| Inputs | text |

## Benchmarks

### CQADupstackPhysicsRetrieval

Domain: scientific · Task: retrieval · Language: en

Duplicate question retrieval from StackExchange Physics

Corpus: 38,314 · Queries: 1,039

**Quality:** map at 10: 0.3410 · mrr at 10: 0.3905 · ndcg at 10: 0.3887

**Performance (L4 b1 c16):** Corpus 21.7K tok/s · Corpus p50 88.1ms · Query 2.5K tok/s · Query p50 68.2ms

[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:** ndcg at 10: 0.3301 · map at 10: 0.2512 · mrr at 10: 0.2343

**Performance (L4 b1 c16):** Corpus 7.4K tok/s · Corpus p50 84.4ms · Query 462 tok/s · Query p50 76.3ms

[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: 0.3126 · mrr at 10: 0.4641 · ndcg at 10: 0.3833

**Performance (L4 b1 c16):** Corpus 18.9K tok/s · Corpus p50 106.6ms · Query 2.4K tok/s · Query p50 71.9ms

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

### LegalBenchConsumerContractsQA

Domain: legal · Task: retrieval · Language: en

Question answering on consumer contracts

Corpus: 153 · Queries: 396

**Variant: default**

**Quality:** ndcg at 10: 0.7773 · map at 10: 0.7300 · mrr at 10: 0.7321

**Performance (L4 b1 c16):** Corpus 42.9K tok/s · Corpus p50 192.4ms · Query 3.6K tok/s · Query p50 70.2ms

**Variant: long_context**

**Quality:** ndcg at 10: 0.8188 · map at 10: 0.7710 · mrr at 10: 0.7718

[Reference](https://huggingface.co/datasets/nguha/legalbench)

### NFCorpus

Domain: medical · Task: retrieval · Language: en

Biomedical literature search from NutritionFacts.org

Corpus: 3,593 · Queries: 323

**Quality:** map at 10: 0.1392 · mrr at 10: 0.5808 · ndcg at 10: 0.3618

**Performance (L4 b1 c16):** Corpus 35.9K tok/s · Corpus p50 101.3ms · Query 1.7K tok/s · Query p50 45.7ms

[Reference](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)

### NanoFiQA2018Retrieval

Domain: finance · Task: retrieval · Language: en

Smaller subset of the FiQA financial QA dataset

**Variant: muvera**

**Quality:** ndcg at 10: 0.4510

**Variant: default**

**Quality:** ndcg at 10: 0.6032 · map at 10: 0.5264 · mrr at 10: 0.6721

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

### 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: 0.0934 · mrr at 10: 0.2875 · ndcg at 10: 0.1608

**Performance (L4 b1 c16):** Corpus 30.1K tok/s · Corpus p50 96.3ms · Query 2.1K tok/s · Query p50 68.6ms

[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:** ndcg at 10: 0.7600 · map at 10: 0.7179 · mrr at 10: 0.7302

**Performance (L4 b1 c16):** Corpus 51.0K tok/s · Corpus p50 82.9ms · Query 3.8K tok/s · Query p50 72.0ms

[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:** ndcg at 10: 0.6568 · map at 10: 0.6232 · mrr at 10: 0.6232

**Performance (L4 b1 c16):** Corpus 26.0K tok/s · Corpus p50 127.7ms · Query 52.9K tok/s · Query p50 91.7ms

[Reference](https://arxiv.org/abs/2407.02883)