Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use lightonai/DenseOn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/DenseOn with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lightonai/DenseOn") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse filesGreat work on this! 🙌
Propose adding a space to the query/document prepend instructions, as that seem to be what is used in the config.
README.md
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## DenseOn
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**DenseOn** is a dense (single-vector) retrieval model built on ModernBERT (149M parameters), trained by [LightOn](https://lighton.ai). It encodes queries and documents independently using cosine similarity with `query:`/`document:` prefixes and CLS pooling.
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DenseOn achieves **56.75** average NDCG@10 on BEIR (14 datasets) and **57.71** on decontaminated BEIR (12 datasets), topping all base-size dense models and outperforming models up to 4x larger.
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Notably it:
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## DenseOn
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**DenseOn** is a dense (single-vector) retrieval model built on ModernBERT (149M parameters), trained by [LightOn](https://lighton.ai). It encodes queries and documents independently using cosine similarity with `query: `/`document: ` prefixes and CLS pooling.
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DenseOn achieves **56.75** average NDCG@10 on BEIR (14 datasets) and **57.71** on decontaminated BEIR (12 datasets), topping all base-size dense models and outperforming models up to 4x larger.
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Notably it:
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