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:
- cd882a19c893dc55f1b05437559b345fcc408a6d9719cb9cad9751b7d70fde4e
- Size of remote file:
- 499 MB
- SHA256:
- 237a188dd89cb8f8787a556a958e47d123d8a5545f8fb983ac22180e1d8fdb26
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.