Instructions to use dzungpham/graphcodebert-code-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dzungpham/graphcodebert-code-classification with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8beb586fef4174b52bca365125334eea7fc90a27082ea8ccd5d22d4ecff0d853
- Size of remote file:
- 5.84 kB
- SHA256:
- 82e524f8f7de87947806acfb17c136195f8d3668b26513da260f1a2f14442156
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.