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 checkpoint-1000/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.01 GB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/b586b98952b0806816208d8f114ea4c2311fafbb/checkpoint-1000/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@b586b98952b0806816208d8f114ea4c2311fafbb/checkpoint-1000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/b586b98952b0806816208d8f114ea4c2311fafbb/checkpoint-1000/optimizer.pt
1.01 GB
- Xet hash:
- ea317c3d1b18668a69793219581bdacc102d7e36fe19839e9b474f2f98e685c9
- Size of remote file:
- 1.01 GB
- SHA256:
- 6bdf390e9c9e5d555f636219141a77d3bb7ad8de7356aaf171bdfbbbf56a7182
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