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:
- aded20e691884274c9afc4b73e465f284e91674e74084a065748eb3e248620f2
- Size of remote file:
- 14.6 kB
- SHA256:
- 75929b49feda2c26b98cb95caa4f592fcde58476e5e3fbab317258120c864ff5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.