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:
- 25992f73ca63e43f11a2870f113778a247b8a64d2e295646ce360007b9426642
- Size of remote file:
- 14.6 kB
- SHA256:
- 453c4c860ef758bf1a759e93274a681f069925df03e10a9fb3b1415d99f5cf2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.