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
Commit History
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Relicense model under Apache 2.0 (#5)
- Relicense model under Apache 2.0 (7c9763204f9876d9c55961786f4fd8c8a3f49878)cf13c81
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Correct max_seq_length in sentence_bert_config.json (#4)
- Correct max_seq_length in sentence_bert_config.json (7bd85976f2dace4a97d99f8266d77679622a7e50) Co-authored-by: Jacob Magnuson <[email protected]>1037887
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