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
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README.md
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@@ -93,4 +93,4 @@ NDCG@10 scores between `zembed-1` and competing embedding models, averaged acros
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| Enterprise | **0.3750** | 0.3600 | 0.2935 | 0.2915 | 0.3224 | 0.3012 | 0.3307 | 0.2213 |
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| **Average** | **0.5561** | **0.5050** | **0.5013** | **0.4957** | **0.4837** | **0.4833** | **0.4727** | **0.4050** |
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Bar chart comparing zembed-1 NDCG@10 scores against competing embedding models across domains
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| Enterprise | **0.3750** | 0.3600 | 0.2935 | 0.2915 | 0.3224 | 0.3012 | 0.3307 | 0.2213 |
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| **Average** | **0.5561** | **0.5050** | **0.5013** | **0.4957** | **0.4837** | **0.4833** | **0.4727** | **0.4050** |
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<img src="assets/zembed_eval_chart.png" alt="Bar chart comparing zembed-1 NDCG@10 scores against competing embedding models across domains" width="1000"/>
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