Instructions to use jfkback/hypencoder.4_layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfkback/hypencoder.4_layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jfkback/hypencoder.4_layer")# Load model directly from transformers import HypencoderDualEncoder model = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.4_layer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 908dfc6f8b5f6c0a914e2f7e3a4386dc36df70219e2ac0de72bb1809b94fae4c
- Size of remote file:
- 521 MB
- SHA256:
- 47b9807932f86a33c7ab7c6746cd04d15813f7193a00069f1c574419691a1f25
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