Sentence Similarity
sentence-transformers
Safetensors
gemma4
feature-extraction
dense
Eval Results (legacy)
Instructions to use shadowlilac/omniembed-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shadowlilac/omniembed-merged with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shadowlilac/omniembed-merged") 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:
- e96a17bb638d7e5c417d4c2abf3cdb5af7a97b50605a250f2f6d84c29b46d1d9
- Size of remote file:
- 32.2 MB
- SHA256:
- a2619fe11b50dbed06ac443c51d757b354d0b62d64baa514404d4e84e6713519
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