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 codebert-base-cross-entropy/checkpoint-2000/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/15a5ed968df7d12f03a29f6d02927efe705abaa5/codebert-base-cross-entropy/checkpoint-2000/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@15a5ed968df7d12f03a29f6d02927efe705abaa5/codebert-base-cross-entropy/checkpoint-2000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/15a5ed968df7d12f03a29f6d02927efe705abaa5/codebert-base-cross-entropy/checkpoint-2000/optimizer.pt
4.74 MB
- Xet hash:
- 3df7960ffa06f1763b79324778c7c2e5a0353f7e317edf148845b15dfabb18c3
- Size of remote file:
- 4.74 MB
- SHA256:
- 2f883eedadd2b52c8954fcbb301c7e3cf1b57fd6cc8147b038ec200d2f69ddd3
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