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Cataloged from lightonai/mDenseOn-unsupervised
All models by lightonai →mDenseOn | mLateOn | DenseOn | LateOn | PyLate | FastPlaid
🎯 TL;DR: The intermediate multilingual dense checkpoint produced by Stage 1 only (unsupervised contrastive pre-training) of the mDenseOn pipeline, trained on a multilingual dataset with 2.8B query–document pairs across nine languages (including 25% cross-lingual pairs). Released as a strong starting point for your own supervised fine-tuning, knowledge distillation, or downstream adaptation.
With DenseOn and LateOn, we demonstrated that an open, carefully curated data recipe can match closed-data retrieval models on English. mDenseOn and mLateOn extend this recipe to multilingual, long-context, and code retrieval.
Rather than independently collecting multilingual corpora from scratch (which would be expensive, uneven across languages, and hard to curate at the same quality), we applied the translate-train approach: machine-translating our validated English data into eight target languages (French, German, Italian, Spanish, Portuguese, Swedish, Norwegian, and Arabic) and adding cross-lingual pairs for cross-lingual alignment.
For more information, please read our multilingual blog post and our English blog post.
mDenseOn-unsupervised is the output of the first stage of the mDenseOn training pipeline. It has been pre-trained on a large, filtered multilingual corpus of approximately 2.8B query–document pairs across nine languages (including 25% cross-lingual pairs) using in-batch contrastive learning, but has not yet been fine-tuned with mined hard negatives.
For most production use cases, you should use the fully-trained mDenseOn instead. This unsupervised checkpoint is intended for:
If your use case permits multi-vector retrieval, also consider mLateOn-unsupervised, the late-interaction counterpart of this checkpoint. The fully-trained mLateOn achieves substantially stronger results, especially on multilingual and long-context tasks, and generalizes to languages outside of the training set.
For more information, please read our multilingual models blog post, our English models blog post and our paper.
query: for queries, document: for documentsFirst install the Sentence Transformers library:
Then you can load this model and run inference.
You can fine-tune this model on your own dataset. This checkpoint is specifically designed as a strong starting point for supervised fine-tuning with mined hard negatives or knowledge distillation, following the recipe described in our multilingual blog post.
We thank Eugene Yang for his feedback on adapting our English study to multilinguality through translate-train. We again thank Xin Zhang, Zach Nussbaum, Tom Aarsen, Bo Wang, Eugene Yang, Benjamin Clavié, Nandan Thakur, Oskar Hallström and Iacopo Poli for their valuable contributions and feedback on the original English study. We thank Orion Weller for building the FineWeb-derived Common Crawl split as well as for his feedback and help. We are grateful to the teams behind Sentence Transformers, BEIR, and MIRACL, and to the open-source retrieval community, in particular the authors of Nomic Embed.
This work was granted access to the HPC resources of IDRIS under GENCI allocations AS011016449, A0181016214, and A0171015706 (Jean Zay supercomputer). We also acknowledge the Barcelona Supercomputing Center (BSC-CNS) for providing access to MareNostrum 5 under EuroHPC AI Factory Fast Lane project EHPC-AIF-2025FL01-445. This project is also supported by the OpenEuroLLM project, co-funded by the Digital Europe Programme under GA no. 101195233.
SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}))pip install -U sentence-transformersfrom sentence_transformers import SentenceTransformer
# Download from the Hubmodel = SentenceTransformer("lightonai/mDenseOn-unsupervised")
# Run inference with multilingual queries and documentsqueries = [ "Quelle planète est connue comme la planète rouge ?", "Which planet is known as the Red Planet?",]documents = [ "Venus wird oft als Zwilling der Erde bezeichnet wegen ihrer ähnlichen Größe.", "Mars, connu pour son apparence rougeâtre, est souvent appelé la planète rouge.", "Marte, conocido por su apariencia rojiza, es a menudo llamado el Planeta Rojo.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.",]
query_embeddings = model.encode(queries, prompt_name="query")document_embeddings = model.encode(documents, prompt_name="document")print(query_embeddings.shape, document_embeddings.shape)# [2, 768] [4, 768]
# Get the similarity scores for the embeddingssimilarities = model.similarity(query_embeddings, document_embeddings)print(similarities)@misc{sourty2026denseonlateonfullyopen, title = {DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search}, author = {Raphaël Sourty and Antoine Chaffin and Paulo Roberto Moura Junior and Amélie Chatelain}, year = {2026}, eprint = {2607.27178}, archivePrefix = {arXiv}, primaryClass = {cs.CL}, url = {https://arxiv.org/abs/2607.27178},}@misc{sourty2026denseonlateon, title={DenseOn with the LateOn: Open State-of-the-Art Single and Multi-Vector Models}, author={Sourty, Raphael and Chaffin, Antoine and Weller, Orion and Moura Junior, Paulo Roberto and Chatelain, Amelie}, year={2026}, howpublished={\url{https://huggingface.co/blog/lightonai/denseon-lateon}},}@inproceedings{DBLP:conf/cikm/ChaffinS25, author = {Antoine Chaffin and Rapha{\"{e}}l Sourty}, editor = {Meeyoung Cha and Chanyoung Park and Noseong Park and Carl Yang and Senjuti Basu Roy and Jessie Li and Jaap Kamps and Kijung Shin and Bryan Hooi and Lifang He}, title = {PyLate: Flexible Training and Retrieval for Late Interaction Models}, booktitle = {Proceedings of the 34th {ACM} International Conference on Information and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November 10-14, 2025}, pages = {6334--6339}, publisher = {{ACM}}, year = {2025}, url = {https://github.com/lightonai/pylate}, doi = {10.1145/3746252.3761608},}@inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084"}