Feature Extraction
Transformers
Safetensors
sentence-transformers
English
qwen3
text-generation
zen
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
- Transformers
How to use zenlm/zen-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zenlm/zen-embedding")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-embedding") model = AutoModelForMultimodalLM.from_pretrained("zenlm/zen-embedding") - 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
- Xet hash:
- 7f26d71aeab40e6bac513acbfab71f48109e92ee379f5fc5fe23dba262364317
- Size of remote file:
- 11.4 MB
- SHA256:
- 83cdf8c3a34f68862319cb1810ee7b1e2c0a44e0864ae930194ddb76bb7feb8d
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