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:
- 24fb21546084cfb8d45113042f9f07b3a922d4d7b9c64c152a6fa95c112b454b
- Size of remote file:
- 5.91 kB
- SHA256:
- a3d448040cc8193c39a3aa43e3972db0566234234cb8950ae351de6781f2f556
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.