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:
- f5ec5e01c7c9f20c958f5801de7c006e3433e13bddf95db3fea8ce4b01913b3d
- Size of remote file:
- 1.01 GB
- SHA256:
- ba982c0b65ea985886ba211d57e0bd9ab51bdf51d494e701f5d6b184e029fcab
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.