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:
- dcb5a6f8b5ef9397a2e005f66b02f6bb3074c8e61adfdb423e3cc9a604871ccd
- Size of remote file:
- 1.38 kB
- SHA256:
- cb7fde5111803012042c93a73aa191336bb6e10b3ad44f6bd1d94fc7008a22b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.