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
| model-00001-of-00002.safetensors filter=lfs diff=lfs merge=lfs -text | |
| model-00002-of-00002.safetensors filter=lfs diff=lfs merge=lfs -text | |
| projections.safetensors filter=lfs diff=lfs merge=lfs -text | |
| tokenizer.json filter=lfs diff=lfs merge=lfs -text | |
| zembed_eval_chart.png filter=lfs diff=lfs merge=lfs -text | |
| assets/zembed_eval_chart.png filter=lfs diff=lfs merge=lfs -text | |