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:
- 088c3d944edca2a3a91fef0b5ec44e6c5f37a6be7454f96f35c897aeaab650dc
- Size of remote file:
- 4.74 MB
- SHA256:
- 1d5f2a4e29cfecb88fea98d32a77638bb186493bafd4d26755ec5399033d3225
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.