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
graphcodebert-code-classification / graphcodebert-swa-from-epoch-1 /checkpoint-1500 /model.safetensors
- Xet hash:
- 5378295df91395716752300c4c9e455509219866e982add64e710bc510b25368
- Size of remote file:
- 499 MB
- SHA256:
- f755acd67a40524d15ea0580335326c203b2747bf4d9d690ee0ba85922d41585
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