Instructions to use jfkback/hypencoder.2_layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfkback/hypencoder.2_layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jfkback/hypencoder.2_layer")# Load model directly from transformers import HypencoderDualEncoder model = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.2_layer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 459f57e938be6e1e9d7f627a2accd706d8ff52fce9b71696af1c44c0f5f22090
- Size of remote file:
- 483 MB
- SHA256:
- 60fd0fb84ac43ea4424884e77bb9bd3de68f6f3e64e75a5c671774df0998823d
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