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:
- 02ec49c64524c2d92f8e0172e04f265ee038629d92682e334204afef656d5180
- Size of remote file:
- 1.47 kB
- SHA256:
- 223ff4ad0572c1075186f0d43c6aebd5581a30e1282be5d602abe8def7241268
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.