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:
- 45a6a6e35cb5b63bf0ecfb8b71ea6cd54b949b9b597f1da2aae1c905062e7949
- Size of remote file:
- 4.74 MB
- SHA256:
- f5e8703837fc2387f895160f1383abea91a1738e73306e935f547aeb1b97c3a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.