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:
- b0a2cc21969fc2131388c5cbbde341f644dd16b52c456c8f0c30195f4fa22621
- Size of remote file:
- 14.6 kB
- SHA256:
- 2b427a0205e14d6d4032ab551a6a7140c9e60c809edd788f977f847d1512d0fb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.