Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use lightonai/DenseOn-unsupervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/DenseOn-unsupervised with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lightonai/DenseOn-unsupervised") 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
File size: 300 Bytes
7c1a495 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"model_type": "SentenceTransformer",
"__version__": {
"sentence_transformers": "5.1.1",
"transformers": "4.57.5",
"pytorch": "2.9.0+cu128"
},
"prompts": {
"query": "query: ",
"document": "document: "
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |