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-1400/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/f6ef07acbf5e482d0551d53e2f8ba9d420e7fbb5/checkpoint-1400/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@f6ef07acbf5e482d0551d53e2f8ba9d420e7fbb5/checkpoint-1400/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/f6ef07acbf5e482d0551d53e2f8ba9d420e7fbb5/checkpoint-1400/training_args.bin
5.84 kB
- Xet hash:
- 49938b630d820b574611dec53cefcf36f0decd106b857aa8093aac0a76668d3c
- Size of remote file:
- 5.84 kB
- SHA256:
- 1ac849e7988017583c1fbea58bf7deca363c8ee46bc62efa3a3a962d250bd5ca
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