Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
multilingual
code search
text-embeddings-inference
🇪🇺 Region: EU
Instructions to use lightonai/mDenseOn-unsupervised with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lightonai/mDenseOn-unsupervised with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lightonai/mDenseOn-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
- Xet hash:
- 2a6a79b9ec43b76acf62767fee87794c7639de2ca61fb0b0e988639423d529c1
- Size of remote file:
- 1.23 GB
- SHA256:
- d3ea443031e94592f9355221508db5da3546825a1e9ba962ea29b5c0b117cb99
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