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 taskA-codebert-base-focal/checkpoint-600/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/b0447775581a3939d2778bd4499e73c852cebb42/taskA-codebert-base-focal/checkpoint-600/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@b0447775581a3939d2778bd4499e73c852cebb42/taskA-codebert-base-focal/checkpoint-600/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/b0447775581a3939d2778bd4499e73c852cebb42/taskA-codebert-base-focal/checkpoint-600/training_args.bin
5.78 kB
- Xet hash:
- 67bd10bfa5409f05cb88a66fdf185b2668c0d14e5a93b3d46d0409cc048b7e9b
- Size of remote file:
- 5.78 kB
- SHA256:
- 25442a0fc63ab39a0b4278f6954102923d048206f65e3a867203c3b922f90a51
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