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/26476e0af80ba0a4d10bb107e7ddbfeef1d6b225/graphcodebert-robust/checkpoint-200/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@26476e0af80ba0a4d10bb107e7ddbfeef1d6b225/graphcodebert-robust/checkpoint-200/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/26476e0af80ba0a4d10bb107e7ddbfeef1d6b225/graphcodebert-robust/checkpoint-200/training_args.bin
5.84 kB
- Xet hash:
- f5ce357be98aae513fa9082012bfbaa1871fd82c73a3e3bcca5e274c6f8596f5
- Size of remote file:
- 5.84 kB
- SHA256:
- 7ec2974753acccea9af7a8eb9c2abfaaba85cdcf89c926488b103f5662876bb0
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