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-1200 /model.safetensors
- Xet hash:
- 5cff7504f62db56c063fa5f62432fa2f2f7424b6c42300bfe271e7c27911978d
- Size of remote file:
- 499 MB
- SHA256:
- 638928033229238d9d8b14410b8c8884341bf5076f986fddea82390a6ad61185
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