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-mixcode-fft/checkpoint-400/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/4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-400/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-400/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-400/training_args.bin
5.84 kB
- Xet hash:
- 7d4c23a79952b7df7bfdc4a79115800a6e06e6e760f637d3d56aca0a5f752ab2
- Size of remote file:
- 5.84 kB
- SHA256:
- 55e805564976941c69ed81dd4f57f02f704352ee18e811a5a0c0eac53999ccb7
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