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-150/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 4.74 MB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-150/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-150/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-150/optimizer.pt
4.74 MB
- Xet hash:
- 936a1fc7e73e2881fe4dd2030fd7c6bebdb65772736da1da708f77f50a007732
- Size of remote file:
- 4.74 MB
- SHA256:
- 639008735bd5220a1a441168bcd7b2a6e6e7e9e2be36e0bb75b4f134f1e97524
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