Instructions to use gsjang/kepri-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsjang/kepri-embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gsjang/kepri-embedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gsjang/kepri-embedding") model = AutoModel.from_pretrained("gsjang/kepri-embedding") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e7834e3b19bae5b52d0f1c7f9da8055634ab9a314f58a233ac795436120236d
- Size of remote file:
- 443 MB
- SHA256:
- bbad31734f3289fb3b4a559c9fe471b2ce2c509b62fd9f14fa9856733f431097
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