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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download graphcodebert-robust/checkpoint-400/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 4.74 MB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/f81d18341368d585a39b4284f61f1501f83bafde/graphcodebert-robust/checkpoint-400/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@f81d18341368d585a39b4284f61f1501f83bafde/graphcodebert-robust/checkpoint-400/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/f81d18341368d585a39b4284f61f1501f83bafde/graphcodebert-robust/checkpoint-400/optimizer.pt
4.74 MB
- Xet hash:
- f331b19b8b939e4e642f33090d50f121f02dfe38dcc27b414b5c7ccff11a0d29
- Size of remote file:
- 4.74 MB
- SHA256:
- 3993e14f8e5395da15ce3350b7a6c24a8b0c21921fd8cce7a29d5175f071b2fc
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