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:
- f2b15f9070dc32838e845e352210a64bc3d79575e7359a5323006552742532a2
- Size of remote file:
- 14.6 kB
- SHA256:
- bf62f8def605a7b37ccdf554f2d761fef6024ce248ea5ced03ac30c2a3bfdf2a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.