mDenseOn-unsupervised

📚 Collection | 📝 Multilingual Blog | 📝 English Blog | 📝 Paper

🎯 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.

About the mDenseOn / mLateOn Family

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

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:

• Researchers studying what each pipeline stage contributes in a multilingual setting
• Practitioners who want to fine-tune on their own domain-specific or language-specific data
• Distillation experiments where you want to start from a strong but un-aligned multilingual base
• Anyone running their own ablations on hard-negative mining strategies or translate-train recipes

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.

Related Checkpoints

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: mmBERT-base
• Maximum Sequence Length: 8,192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Pooling: [CLS] token
• Prompts: query: for queries, document: for documents
• Languages: English, French, German, Italian, Spanish, Portuguese, Swedish, Norwegian, Arabic
• License: Apache 2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

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})
)

Usage

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