Instructions to use jfkback/hypencoder.6_layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfkback/hypencoder.6_layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jfkback/hypencoder.6_layer")# Load model directly from transformers import HypencoderDualEncoder model = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.6_layer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e2f3d8a59076c765b55cc43a11d960f7994f2972f7606a404ad7b194f9ae266
- Size of remote file:
- 559 MB
- SHA256:
- 3018443299cdcbf73ef176658044502a0b21fbd749e97eb24d23cbc854635508
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