---
title: "jina-embeddings-v3: Multilingual Embeddings With Task LoRA"
description: "The authors present jina-embeddings-v3, a 570M-parameter multilingual encoder built on… 42 references, cited by 91 papers here."
authors: ["Saba Sturua", "Isabelle Mohr", "Mohammad Kalim Akram", "Michael Gunther", "Bo Wang", "Markus Krimmel", "Feng Wang", "Georgios Mastrapas", "Andreas Koukounas", "Nan Wang", "Han Xiao"]
year: 2024
venue: "arXiv:2409.10173"
source: "https://arxiv.org/abs/2409.10173"
doi: "10.48550/arxiv.2409.10173"
arxiv: "2409.10173"
licence: "CC BY-NC-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sturua-2024-jina-embeddings-v3-multilingual-embeddings/"
---
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# jina-embeddings-v3: Multilingual Embeddings With Task LoRA

**Authors:** [Saba Sturua](https://md.research.elamur.ai/free/en/authors/saba-sturua/), [Isabelle Mohr](https://md.research.elamur.ai/free/en/authors/isabelle-mohr/), [Mohammad Kalim Akram](https://md.research.elamur.ai/free/en/authors/mohammad-kalim-akram/), [Michael Gunther](https://md.research.elamur.ai/free/en/authors/michael-gunther/), [Bo Wang](https://md.research.elamur.ai/free/en/authors/bo-wang/), [Markus Krimmel](https://md.research.elamur.ai/free/en/authors/markus-krimmel/), [Feng Wang](https://md.research.elamur.ai/free/en/authors/feng-wang/), [Georgios Mastrapas](https://md.research.elamur.ai/free/en/authors/georgios-mastrapas/), Andreas Koukounas, [Nan Wang](https://md.research.elamur.ai/free/en/authors/nan-wang/) and [Han Xiao](https://md.research.elamur.ai/free/en/authors/han-xiao/) · **Year:** 2024 · **Venue:** arXiv:2409.10173 · **Licence:** [CC BY-NC-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2409.10173) · [PDF](https://arxiv.org/pdf/2409.10173) · [DOI 10.48550/arxiv.2409.10173](https://doi.org/10.48550/arxiv.2409.10173) · [arXiv 2409.10173](https://arxiv.org/abs/2409.10173) · [OpenAlex](https://openalex.org/W4403746919)

**Citations:** 18 citations (OpenAlex, 2026-10-14) · [91](https://md.research.elamur.ai/free/en/papers/sturua-2024-jina-embeddings-v3-multilingual-embeddings/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
General-purpose text embedding models often still need task-specific fine-tuning. LLM-based embedders, typically around 7B parameters, are expensive to deploy for small gains. The authors present jina-embeddings-v3, a 570M-parameter multilingual encoder built on XLM-RoBERTa, keeping its original tokenizer. They replace absolute position embeddings with RoPE, so the model accepts inputs of up to 8192 tokens, and they use FlashAttention 2 for efficiency.

Instead of relying only on instruction prefixes, the model carries small task-specific LoRA adapters. One adapter is selected per input based on the task: retrieval (with separate query and passage adapters), clustering, classification or text matching. Training starts from XLM-RoBERTa weights with multilingual MLM pre-training, then moves to contrastive embedding fine-tuning. Synthetic data targets four observed retrieval failure types, and Matryoshka Representation Learning lets users shorten the vectors.

On MTEB, the model beats recent OpenAI and Cohere embeddings on English tasks and multilingual-e5-large-instruct on multilingual tasks. This makes it a compact, production-friendly alternative to LLM embedders.

## Key points
- The model has 570M parameters and a 1024-dimensional default output. Its five LoRA adapters (rank 4, four task types) make up under 3% of all weights.
- The rotary base frequency is 10,000 during training and is raised to 20,000 at inference. This helps on long texts without hurting short-text results.
- e5-mistral-7b-instruct is about 12x larger, with 4096-dim vectors, but is only about 1% better on English MTEB.
- MRL ablation: average retrieval nDCG@10 is 63.35 at 1024 dims, 61.64 at 128 and 52.54 at 32. For STS, the score stays near 77 at 64 dims and above, falling to 76.35 at 32.
- Retrieval failure cases are released as jinaai/retrieval-failure-examples. Using two retrieval adapters averaged 45.98 / 45.62 nDCG@10 (with / without instructions), versus 45.20 / 43.92 for a single adapter.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Jina AI](https://md.research.elamur.ai/free/en/companies/jina-ai/) — Company, Berlin, Germany · 2 works · 184 citations

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🧭 Topics (1): [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 882 works

## Cite

Cite as: Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, Michael Gunther, Bo Wang, Markus Krimmel, Feng Wang, Georgios Mastrapas, Andreas Koukounas, Nan Wang and Han Xiao. 2024. jina-embeddings-v3: Multilingual Embeddings With Task LoRA. arXiv:2409.10173.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{sturua2024jina,
  title = {{jina-embeddings-v3: Multilingual Embeddings With Task LoRA}},
  author = {Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, Michael Gunther, Bo Wang, Markus Krimmel, Feng Wang, Georgios Mastrapas, Andreas Koukounas, Nan Wang and Han Xiao},
  year = {2024},
  doi = {10.48550/arxiv.2409.10173},
  eprint = {2409.10173},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2409.10173}
}
```

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*Updated 2 November 2026*