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 modules.json from zenlm/zen-embedding-2: direct link, hf CLI and curl.
- Browser
- Download file 413 Bytes
-
https://huggingface.co/zenlm/zen-embedding-2/resolve/main/modules.json
- Command line
-
hf download hf://zenlm/zen-embedding-2/modules.json
-
curl -L -o modules.json https://huggingface.co/zenlm/zen-embedding-2/resolve/main/modules.json
413 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.base.modules.normalize.Normalize" | |
| } | |
| ] |