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:
- b5bfdc2e86951afc49d5a00d84d70981b412f857378a6812da0833838e7bbcb1
- Size of remote file:
- 14.6 kB
- SHA256:
- 36c90ae3575630687b6a7d64bf93dded50adb1dbab4b74db0c9cdd2945f93577
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.