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/rng_state.pth from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 14.6 kB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-400/rng_state.pth
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-400/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-400/rng_state.pth
14.6 kB
- Xet hash:
- cb2f6ca8c1cadb6afd6843cd4fcbfc4688afa3a3e4c59f5c1ca748fac075edce
- Size of remote file:
- 14.6 kB
- SHA256:
- 767451645f91f6b3b01eeb7b622f9415eb0e210eaf2f1868fd8984c503d39ac3
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