Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
dense
multilingual
code search
text-embeddings-inference
🇪🇺 Region: EU
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
- Xet hash:
- 2b46515025016fb401e35e4df9e3fdc0cb72a92a4098fc5b2db1652ebf6d37d9
- Size of remote file:
- 34.4 MB
- SHA256:
- a086e6a7efb90bbab8e379a63f3463fb13e6256ebec3343c1f1612cab693935b
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