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:
- 10b2c9139f6327deab75029e949da35e021891d6fee44e44ec77d357f2bf8f46
- Size of remote file:
- 1.38 kB
- SHA256:
- 54e30e6a2fc2de666ad3bd0097b38b5bd243339a48d5061ef4ab8fe7480885c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.