Instructions to use hayeonkim/uncha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hayeonkim/uncha with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hayeonkim/uncha") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hayeonkim/uncha", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag and GitHub link
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community science team.
This PR improves the model card for UNCHA by:
- Moving the metadata to a proper YAML frontmatter section.
- Adding the
pipeline_tag: zero-shot-image-classificationto the metadata to help users find the model when filtering by task. - Adding a link to the official GitHub repository.
- Linking the paper to its arXiv page.
These changes will improve the visibility and usability of the model on the Hub.
hayeonkim changed pull request status to merged