Instructions to use ayanami-kitasan/code-pruner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayanami-kitasan/code-pruner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ayanami-kitasan/code-pruner")# Load model directly from transformers import SwePrunerForCodeCompression model = SwePrunerForCodeCompression.from_pretrained("ayanami-kitasan/code-pruner", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add model card and metadata
#1
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community team. I noticed that this repository didn't have a model card, so I've opened this PR to add one. It includes relevant metadata like the pipeline tag, library name, and base model, as well as links to the paper and GitHub repository to make the model more discoverable.
Thanks for your PR! Actually I've been a bit overwhelmed recently by the process of opening up our work. Your pull request has been of great help to us.
Merged.
ayanami-kitasan changed pull request status to merged