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_type": "SentenceTransformer", | |
| "prompts": { | |
| "query": "<|im_start|>system\nquery<|im_end|>\n<|im_start|>user\n", | |
| "document": "<|im_start|>system\ndocument<|im_end|>\n<|im_start|>user\n" | |
| }, | |
| "suffix": "<|im_end|>\n", | |
| "default_prompt_name": "document", | |
| "similarity_fn_name": "cosine" | |
| } |