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Cataloged from lightonai/mLateOn-unsupervised
All models by lightonai βmDenseOn | mLateOn | DenseOn | LateOn | PyLate | FastPlaid
π― TL;DR: The intermediate multilingual ColBERT checkpoint produced by Stage 1 only (unsupervised contrastive pre-training) of the mLateOn pipeline, trained on a multilingual dataset with 2.8B queryβdocument 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 models blog post, our English models blog post and our paper.
mLateOn-unsupervised is the output of the first stage of the mLateOn training pipeline. It has been pre-trained on a large, filtered multilingual corpus of approximately 2.8B queryβdocument 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 mLateOn instead. This unsupervised checkpoint is intended for:
First install the PyLate library:
Use this model with PyLate to index and retrieve documents. The index uses FastPlaid for efficient similarity search.
Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries β including cross-lingual retrieval:
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use the rank function and pass the queries and documents to rerank:
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 ClaviΓ©, Nandan Thakur, Oskar HallstrΓΆm 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.
ColBERT( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'ModernBertModel'}) (1): Dense({'in_features': 768, 'out_features': 1536, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': True}) (2): Dense({'in_features': 1536, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': True}) (3): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False}))pip install -U pylatefrom pylate import indexes, models, retrieve
# Step 1: Load the ColBERT modelmodel = models.ColBERT( model_name_or_path="lightonai/mLateOn-unsupervised",)
# Step 2: Initialize the PLAID indexindex = indexes.PLAID( index_folder="pylate-index", index_name="index", override=True, # This overwrites the existing index if any)
# Step 3: Encode the documents (supports multilingual content)documents_ids = ["1", "2", "3", "4"]documents = [ "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Mars, connu pour son apparence rougeÒtre, est souvent appelé la planète rouge.", "Mars, bekannt für sein râtliches Erscheinungsbild, wird oft als der Rote Planet bezeichnet.", "Venus is often called Earth's twin because of its similar size and proximity.",]
documents_embeddings = model.encode( documents, batch_size=32, is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries show_progress_bar=True,)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding idsindex.add_documents( documents_ids=documents_ids, documents_embeddings=documents_embeddings,)# To load an index, simply instantiate it with the correct folder/name and without overriding itindex = indexes.PLAID( index_folder="pylate-index", index_name="index",)# Step 1: Initialize the ColBERT retrieverretriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries (can be in any supported language)queries_embeddings = model.encode( [ "Quelle planète est connue comme la planète rouge ?", # French query "Which planet is the Red Planet?", # English query ], batch_size=32, is_query=True, # Ensure that it is set to True to indicate that these are queries show_progress_bar=True,)
# Step 3: Retrieve top-k documents (returns matches across all languages)scores = retriever.retrieve( queries_embeddings=queries_embeddings, k=10, # Retrieve the top 10 matches for each query)from pylate import rank, models
queries = [ "Quelle planète est la planète rouge ?", "What is the largest planet in the solar system?",]
documents = [ ["Mars ist der Rote Planet.", "Venus ist der Zwilling der Erde."], ["Jupiter is the largest planet.", "Saturn is known for its rings.", "Mars is the Red Planet."],]
documents_ids = [ [1, 2], [1, 3, 2],]
model = models.ColBERT( model_name_or_path="lightonai/mLateOn-unsupervised",)
queries_embeddings = model.encode( queries, is_query=True,)
documents_embeddings = model.encode( documents, is_query=False,)
reranked_documents = rank.rerank( documents_ids=documents_ids, queries_embeddings=queries_embeddings, documents_embeddings=documents_embeddings,)@misc{sourty2026denseonlateonfullyopen, title = {DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search}, author = {RaphaΓ«l Sourty and Antoine Chaffin and Paulo Roberto Moura Junior and AmΓ©lie 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"}