Feature Extraction
sentence-transformers
Safetensors
English
Chinese
qwen3
zen
zen-embedding
zenlm
hanzo
embedding
retrieval
text-embeddings-inference
Instructions to use zenlm/zen-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zenlm/zen-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zenlm/zen-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
Download tokenizer.json from zenlm/zen-embedding: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/zenlm/zen-embedding/resolve/refs%2Fpr%2F2/tokenizer.json
- Command line
-
hf download hf://zenlm/zen-embedding@refs/pr/2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/zenlm/zen-embedding/resolve/refs%2Fpr%2F2/tokenizer.json
11.4 MB
- Xet hash:
- 7f26d71aeab40e6bac513acbfab71f48109e92ee379f5fc5fe23dba262364317
- Size of remote file:
- 11.4 MB
- SHA256:
- 83cdf8c3a34f68862319cb1810ee7b1e2c0a44e0864ae930194ddb76bb7feb8d
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