---
title: "Multilingual E5 Text Embeddings — Wang 2024"
description: "Most text embedding models are trained only on English, so they are of limited use for retrieval and semantic similarity in other languages."
authors: ["Liang Wang", "Nan Yang", "Xiaolong Huang", "Linjun Yang", "Rangan Majumder", "Furu Wei"]
year: 2024
venue: "arXiv:2402.05672"
source: "https://arxiv.org/abs/2402.05672"
doi: "10.48550/arxiv.2402.05672"
arxiv: "2402.05672"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2024-multilingual-e5-text-embeddings-technical/"
---
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# Multilingual E5 Text Embeddings: A Technical Report

**Authors:** [Liang Wang](https://md.research.elamur.ai/free/en/authors/liang-wang/), [Nan Yang](https://md.research.elamur.ai/free/en/authors/nan-yang/), [Xiaolong Huang](https://md.research.elamur.ai/free/en/authors/xiaolong-huang/), [Linjun Yang](https://md.research.elamur.ai/free/en/authors/linjun-yang/), [Rangan Majumder](https://md.research.elamur.ai/free/en/authors/rangan-majumder/), [Furu Wei](https://md.research.elamur.ai/free/en/authors/furu-wei/) · **Year:** 2024 · **Venue:** arXiv:2402.05672 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2402.05672) · [PDF](https://arxiv.org/pdf/2402.05672) · [DOI 10.48550/arxiv.2402.05672](https://doi.org/10.48550/arxiv.2402.05672) · [arXiv 2402.05672](https://arxiv.org/abs/2402.05672) · [OpenAlex](https://openalex.org/W4391709827)

**Citations:** 40 citations (openalex-snapshot · 2026-10-14) · [302](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Most text embedding models are trained only on English, so they are of limited use for retrieval and semantic similarity in other languages. This report describes the multilingual E5 family (mE5-small, mE5-base, mE5-large), released openly in mid-2023, plus a newer instruction-tuned variant, mE5-large-instruct. Training follows the two-stage recipe of the English E5 models. In the first stage, the models continue pre-training with a contrastive objective (InfoNCE with in-batch negatives) on about 1 billion weakly paired multilingual texts. Sources include Wikipedia, mC4, NLLB, Reddit and xP3. In the second stage, they are fine-tuned on about 1.6M labeled pairs, using mined hard negatives and distillation from a cross-encoder. The instruct model also uses about 500k synthetic examples generated by GPT-3.5/4, covering 93 languages. The models are evaluated on English MTEB, on MIRACL retrieval in 16 languages and on bitext mining in more than 100 languages. The instruct model matches or slightly beats strong English-only and commercial multilingual models. For Ukrainian and other non-English work, the free weights are a practical option for search, clustering and similarity.

## Key points
- **Pre-training setup:** batch size 32k for 30k steps, about 1B text pairs. The fine-tuning mix (about 1.6M examples) includes MS-MARCO, NQ, TriviaQA, SQuAD, NLI, DuReader, HotpotQA, Mr. TyDi and MIRACL.
- **MIRACL dev, average over 16 languages (nDCG@10):** mE5-small 60.8, base 62.3, large 66.5 and large-instruct 65.7. For comparison, BM25 scores 39.3 and mDPR scores 41.5, even though mDPR was fine-tuned on MIRACL. Recall@100 for the mE5 models reaches up to 94.6.
- **English MTEB (56 datasets):** mE5-large-instruct scores 64.4. Cohere multilingual-v3 scores 64.0, the English-only BGE large-en-v1.5 scores 64.2 and LaBSE scores 45.2.
- **Bitext mining:** on Tatoeba (112 languages), mE5-large-instruct scores 83.8 and LaBSE scores 81.1. The authors credit the instruct model's lead to the wider language coverage of the synthetic data. On BUCC 2018 the instruct model scores 99.0.
- **Release:** all models are available through the microsoft/unilm repository on GitHub (e5 directory). The smaller sizes score lower but are cheaper to run and store.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) · [AI & machine learning](https://md.research.elamur.ai/free/en/shelves/ai/)

## Cited by

Cited by

302 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 293 not yet.

- [Intertextual Parallel Detection in Biblical Hebrew: A Transformer-Based Benchmark](https://md.research.elamur.ai/free/en/papers/hbo-2025-smiley-intertextual-parallel-detection-biblical-hebrew/)
- [NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew](https://md.research.elamur.ai/free/en/papers/langstream-2025-shmidman-neodictabert-pushing-frontier-bert-models/)
- [Mitigating Language Bias in Multilingual Sentence Embeddings for Cross-Lingual Similarity Estimation](https://md.research.elamur.ai/free/en/papers/nonomura-2026-mitigating-language-bias-multilingual-sentence/)
- [MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages](https://md.research.elamur.ai/free/en/papers/smart-2026-multiwikiqa-reading-comprehension-benchmark-300/)
- [An Extreme Multi-label Text Classification (XMTC) Library Dataset: What If We Took “Use of Practical AI in Digital Libraries” Seriously?](https://md.research.elamur.ai/free/en/papers/souza-2026-extreme-multi-label-text-classification/)
- [Uncertainty Quantification for Evaluating Gender Bias in Machine Translation](https://md.research.elamur.ai/free/en/papers/staliunaite-2026-uncertainty-quantification-evaluating-gender-bias/)
- [Improving Text Embeddings with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-improving-text-embeddings-large-language/)
- [Measuring Idiomaticity in Text Embedding Models with epsilon-compositionality](https://md.research.elamur.ai/free/en/papers/wold-2026-measuring-idiomaticity-text-embedding-models/)
- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/)

## Cite

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

```bibtex
@misc{wang2024multilingual,
  title = {{Multilingual E5 Text Embeddings: A Technical Report}},
  author = {Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
  year = {2024},
  doi = {10.48550/arxiv.2402.05672},
  eprint = {2402.05672},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.05672}
}
```

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