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-1100 /model.safetensors
- Xet hash:
- 7fae7eba278f626c8934ae403997fd4ec7f785ed1e5bc656dd23bcb7dc398c0a
- Size of remote file:
- 499 MB
- SHA256:
- 3c9417a2936e7f0ce73d8c66376e5076e086152e1065d08a19c88e74a6d9d60b
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