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
Add pipeline tag and license
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- adding the appropriate
pipeline_tagto the model, ensuring the model can be found at https://huggingface.co/models?pipeline_tag=feature-extraction - adding the license type.
- adding a quick start code snippet.
jfkback changed pull request status to merged