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:
- 51bfbf01aad6cd11ea2f373dacf7f3f5a324012fbdd73bb2823908d948ee8c7e
- Size of remote file:
- 4.74 MB
- SHA256:
- 6463d936c9004d9610dc37c3709533220325775d0fee173d8a65fa040c277349
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.