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
Add license and pipeline tag
#3
by nielsr HF Staff - opened
This PR adds the relevant pipeline tag, ensuring the model can be found at https://huggingface.co/models?pipeline_tag=feature-extraction&sort=trending.
It also adds the relevant license.
jfkback changed pull request status to merged