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:
- d97a22bac282f5e1cda82d2dac6edc23483970a23d3efed44ff177a5f73b91ca
- Size of remote file:
- 504 MB
- SHA256:
- d4ef92bc96bd8e16e5672edc0626f0d2844af2b2f0171a6e52ec38d93964fa9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.