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-300/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/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-mixcode-fft/checkpoint-300/rng_state.pth
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-mixcode-fft/checkpoint-300/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-mixcode-fft/checkpoint-300/rng_state.pth
14.6 kB
- Xet hash:
- 523c15ba8b0d22ec4acf769879342b57dc22a19913fa3dfdd8f80594e87f74c3
- Size of remote file:
- 14.6 kB
- SHA256:
- e1dfb987e1a050c893d93290f8e85866cd8c1fac4f3944f09511cfe099a3e447
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