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-200/training_args.bin from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/f81d18341368d585a39b4284f61f1501f83bafde/graphcodebert-robust/checkpoint-200/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@f81d18341368d585a39b4284f61f1501f83bafde/graphcodebert-robust/checkpoint-200/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/f81d18341368d585a39b4284f61f1501f83bafde/graphcodebert-robust/checkpoint-200/training_args.bin
5.84 kB
- Xet hash:
- 40bee5cfca9a7b58eb921a9fbb2a40141fd4c5d477607ef2e98b1463b648a853
- Size of remote file:
- 5.84 kB
- SHA256:
- 9ff659b85d84ec0bae53596bc271ba773db9c463626db0f13fd8e747f433dad4
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