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:
- 8dc5d8c2807daf76241af16a5f64777ea5a5c5031744aedcb896afed582c853c
- Size of remote file:
- 499 MB
- SHA256:
- 75ec427b92df30abfd117ca61bf8855a95bff5b8e2f300c83f23131aa83f89a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.