Feature Extraction
sentence-transformers
Safetensors
multilingual
embedding_gemma2
zen
zen-embedding
zenlm
hanzo
embedding
multimodal-embedding
vision
audio
video
retrieval
matryoshka
Instructions to use zenlm/zen-embedding-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zenlm/zen-embedding-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zenlm/zen-embedding-2") 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
Download 2_Normalize/config.json from zenlm/zen-embedding-2: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/zenlm/zen-embedding-2/resolve/main/2_Normalize/config.json
- Command line
-
hf download hf://zenlm/zen-embedding-2/2_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/zenlm/zen-embedding-2/resolve/main/2_Normalize/config.json
97 Bytes
| { | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |