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
Update README.md
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README.md
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@@ -42,7 +42,6 @@ from sentence_transformers import SentenceTransformer
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model = SentenceTransformer(
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"zeroentropy/zembed-1",
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trust_remote_code=True,
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# truncate_dim=640, # Optional: Reduce dimensionality from 2560 to {1280, 640, 320, 160, 80, 40}
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model_kwargs={"torch_dtype": "bfloat16"},
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)
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model = SentenceTransformer(
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"zeroentropy/zembed-1",
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trust_remote_code=True,
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model_kwargs={"torch_dtype": "bfloat16"},
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)
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