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-2000/training_args.bin from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/218aef1c8c3bdf2f70ee1c2818bd793f99758f8d/checkpoint-2000/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@218aef1c8c3bdf2f70ee1c2818bd793f99758f8d/checkpoint-2000/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/218aef1c8c3bdf2f70ee1c2818bd793f99758f8d/checkpoint-2000/training_args.bin
5.78 kB
- Xet hash:
- d97e7cb9176824ed40ed3681300eb1f7ecdfd83ec637fde2358e4e4b81fcb3bc
- Size of remote file:
- 5.78 kB
- SHA256:
- 6eb85eb718e835a70af2767820254782432279c0d0625995a842ad21f13fca44
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.