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lightonai
/
DenseOn

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
text-embeddings-inference
🇪🇺 Region: EU
Model card Files Files and versions
xet
Community
1

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]
  • Notebooks
  • Google Colab
  • Kaggle
DenseOn
600 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 9 commits
NohTow's picture
NohTow
Update README.md
41b4bba verified 16 days ago
  • 1_Pooling
    Add new SentenceTransformer model about 2 months ago
  • .gitattributes
    1.52 kB
    initial commit about 2 months ago
  • README.md
    15.3 kB
    Update README.md 16 days ago
  • config.json
    1.21 kB
    Add new SentenceTransformer model about 2 months ago
  • config_sentence_transformers.json
    300 Bytes
    Add new SentenceTransformer model about 2 months ago
  • model.safetensors
    596 MB
    xet
    Add new SentenceTransformer model about 2 months ago
  • modules.json
    229 Bytes
    Add new SentenceTransformer model about 2 months ago
  • sentence_bert_config.json
    57 Bytes
    Add new SentenceTransformer model about 2 months ago
  • special_tokens_map.json
    694 Bytes
    Add new SentenceTransformer model about 2 months ago
  • tokenizer.json
    3.58 MB
    Add new SentenceTransformer model about 2 months ago
  • tokenizer_config.json
    21 kB
    Add new SentenceTransformer model about 2 months ago