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-base-lowLR-highBatchSize /checkpoint-550 /model.safetensors
- Xet hash:
- 85fb5dc8a7ca15bb04dfab26c7357cbe884dbc7212a4f141d1c0a5e24a2f3a0f
- Size of remote file:
- 499 MB
- SHA256:
- 3522ebeb3e76886d055b63e7dbb614e25d4eb7eaa31fe407b0f6ce724a359f2f
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