Instructions to use jfkback/hypencoder.8_layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfkback/hypencoder.8_layer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jfkback/hypencoder.8_layer")# Load model directly from transformers import HypencoderDualEncoder model = HypencoderDualEncoder.from_pretrained("jfkback/hypencoder.8_layer", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and usage example
#1
by nielsr HF Staff - opened
This PR ensures the model can be found at https://huggingface.co/models?pipeline_tag=feature-extraction&sort=trending. It also adds a sample usage from the Github README.
jfkback changed pull request status to merged