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lightonai
/
mDenseOn

Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
multilingual
code search
text-embeddings-inference
🇪🇺 Region: EU
Model card Files Files and versions
xet
Community

Instructions to use lightonai/mDenseOn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use lightonai/mDenseOn with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("lightonai/mDenseOn")
    
    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]
  • Notebooks
  • Google Colab
  • Kaggle
mDenseOn
1.26 GB
Ctrl+K
Ctrl+K
  • 6 contributors
History: 5 commits
Paulo de Moura
Update citation to arXiv paper and add arxiv tag
a5fdb00 about 20 hours ago
  • 1_Pooling
    Initial commit 1 day ago
  • .gitattributes
    1.57 kB
    Initial commit 1 day ago
  • README.md
    20 kB
    Update citation to arXiv paper and add arxiv tag about 20 hours ago
  • config.json
    1.93 kB
    Initial commit 1 day ago
  • config_sentence_transformers.json
    299 Bytes
    Initial commit 1 day ago
  • model.safetensors
    1.23 GB
    xet
    Initial commit 1 day ago
  • modules.json
    277 Bytes
    Initial commit 1 day ago
  • sentence_bert_config.json
    241 Bytes
    Initial commit 1 day ago
  • tokenizer.json
    34.4 MB
    xet
    Initial commit 1 day ago
  • tokenizer_config.json
    662 Bytes
    Initial commit 1 day ago