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:
- e8f2a088e91c3ab27d1fb988be56f2fb32e785296684cffeffb560d802d5802d
- Size of remote file:
- 14.6 kB
- SHA256:
- 1319a6a84eb040363f0531831368126a08d22a17798adaa3582049afe5059f05
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.