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:
- e381e46b29a0e828b52a8ce2cf8c0b1b8a55f7933a916a04cd1b93560fb92da1
- Size of remote file:
- 14.6 kB
- SHA256:
- fd00cb3dea4e903772dc88c19c407f915a129437b8549ec29c11a469c15258c7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.