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-950 /model.safetensors
- Xet hash:
- 41083013bc9f73e6a92854afee748e6b4279fb380f1cbe2266d4cb07e69a7e90
- Size of remote file:
- 499 MB
- SHA256:
- 32474c7a7a43bc675b526d377e3dc3bb6b11d03b94507ab957637a7b1849f0c2
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