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:
- d1fc628350d77da5fcc14843f77108296a0cbe54e48a4ee627b73770615488e8
- Size of remote file:
- 14.6 kB
- SHA256:
- cc41893e18124a5b4346e5ad2eec904a9b13636e7df7f9d4e28520206d9aac00
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.