ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World

TL;DR

ML-Embed introduces a suite of inclusive, efficient multilingual embedding models built on a novel 3D-ML framework, addressing computational costs, linguistic diversity, and transparency issues in text embeddings.

Contribution

The paper presents ML-Embed and the 3D-ML framework, combining efficiency, multilingual coverage, and transparency, with models and data openly released for reproducibility.

Findings

Models set new records on 9 of 17 MTEB benchmarks.

Strong performance in low-resource languages.

Efficient across the entire model lifecycle.

Abstract

The development of high-quality text embeddings is increasingly drifting toward an exclusionary future, defined by three critical barriers: prohibitive computational costs, a narrow linguistic focus that neglects most of the world's languages, and a lack of transparency from closed-source or open-weight models that stifles research. To dismantle these barriers, we introduce ML-Embed, a suite of inclusive and efficient models built upon a new framework: 3-Dimensional Matryoshka Learning (3D-ML). Our framework addresses the computational challenge with comprehensive efficiency across the entire model lifecycle. Beyond the storage benefits of Matryoshka Representation Learning (MRL) and flexible inference-time depth provided by Matryoshka Layer Learning (MLL), we introduce Matryoshka Embedding Learning (MEL) for enhanced parameter efficiency. To address the linguistic challenge, we curate a massively multilingual dataset and train a suite of models ranging from 140M to 8B parameters. In a direct commitment to transparency, we release all models, data, and code. Extensive evaluation on 430 tasks demonstrates that our models set new records on 9 of 17 evaluated MTEB benchmarks, with particularly strong results in low-resource languages, providing a reproducible blueprint for building globally equitable and computationally efficient AI systems.