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 codebert-base-cross-entropy/checkpoint-4000/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/caed3284efcf0158a73d4482c8e16c0eb809cc30/codebert-base-cross-entropy/checkpoint-4000/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@caed3284efcf0158a73d4482c8e16c0eb809cc30/codebert-base-cross-entropy/checkpoint-4000/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/caed3284efcf0158a73d4482c8e16c0eb809cc30/codebert-base-cross-entropy/checkpoint-4000/training_args.bin
5.84 kB
- Xet hash:
- fe0fc63992ec1230de18c10064de5487a1b6fe665a791d74873013d046f1b1c0
- Size of remote file:
- 5.84 kB
- SHA256:
- dbdc2f4c9cd8ad671ca740ab491512a481c34d40fafc15bd45db45cd63388bf1
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