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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add license and pipeline tag
#1
by nielsr HF Staff - opened
This PR adds the feature-extraction pipeline tag to the model card, ensuring the model is findable at https://huggingface.co/models?pipeline_tag=feature-extraction. It also adds the Apache 2.0 license, as specified in the Github repository.
jfkback changed pull request status to merged