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:
- d64e5b411c65400118693e92fd62446fe9743d949c06d2ee51ddac71361e6b4e
- Size of remote file:
- 14.6 kB
- SHA256:
- ef2287c5d715c4c2b1f2475cc59ae767683b80b8c74ca678c03ca174ce5f0e2f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.