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-1200/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-1200/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@26476e0af80ba0a4d10bb107e7ddbfeef1d6b225/graphcodebert-robust/checkpoint-1200/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/26476e0af80ba0a4d10bb107e7ddbfeef1d6b225/graphcodebert-robust/checkpoint-1200/training_args.bin
5.84 kB
- Xet hash:
- 7d301c6a0523e548e769d203ec3b63a3a2313ed5dc6a5b7847492393c7de5c33
- Size of remote file:
- 5.84 kB
- SHA256:
- 64c0b8a6d392bd2e7b64d7504ce486a51b83e4079e79341886020b90ee199ffd
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