Feature Extraction
sentence-transformers
Safetensors
English
multilingual
qwen3
finance
legal
healthcare
code
stem
medical
text-embeddings-inference
Instructions to use zeroentropy/zembed-1-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zeroentropy/zembed-1-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zeroentropy/zembed-1-embedding") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc06e231d54c370199189a8e58a5ddd4099e5e466af04d389b4ac4edf6a16088
- Size of remote file:
- 17.5 MB
- SHA256:
- c2857f09a857d564c78224cdc7baa763773fa96475dd7b9b29d598756b61d083
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