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 checkpoint-600/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/ceed5bc3d8ed98c6c1390765aeb1882e90028be2/checkpoint-600/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@ceed5bc3d8ed98c6c1390765aeb1882e90028be2/checkpoint-600/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/ceed5bc3d8ed98c6c1390765aeb1882e90028be2/checkpoint-600/optimizer.pt
4.74 MB
- Xet hash:
- 45a6a6e35cb5b63bf0ecfb8b71ea6cd54b949b9b597f1da2aae1c905062e7949
- Size of remote file:
- 4.74 MB
- SHA256:
- f5e8703837fc2387f895160f1383abea91a1738e73306e935f547aeb1b97c3a0
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