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-500/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/b2b84a4618aa72207ba6d5a71f69923baade1d82/checkpoint-500/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@b2b84a4618aa72207ba6d5a71f69923baade1d82/checkpoint-500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/b2b84a4618aa72207ba6d5a71f69923baade1d82/checkpoint-500/optimizer.pt
1.01 GB
- Xet hash:
- a9c26e8418f21683eacc9e83493daf2b975e58b2731aea904b5509c49bae7a03
- Size of remote file:
- 1.01 GB
- SHA256:
- 47d4ef5b08e045fc8d0e273d93a6504ca621663aabe70342e6b7477337a51dea
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