Feature Extraction
Transformers
PyTorch
Safetensors
English
bert
zen
zenlm
hanzo
zen3
embedding
retrieval
text-embeddings-inference
Instructions to use zenlm/zen3-embedding-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenlm/zen3-embedding-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zenlm/zen3-embedding-medium")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("zenlm/zen3-embedding-medium") model = AutoModel.from_pretrained("zenlm/zen3-embedding-medium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de6107fcbde14c7512f174d3a2a1ad5a1134f927865a7c1f266b31752e998480
- Size of remote file:
- 1.34 GB
- SHA256:
- 51e14ed95fb897ba8eee3c6d9b5fb4323229a897caaf34053c1b7639b31c1ac4
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