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-rdrop/checkpoint-1000/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/b74eaa6f2792249a25994c9bdd704c51ea169454/graphcodebert-rdrop/checkpoint-1000/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@b74eaa6f2792249a25994c9bdd704c51ea169454/graphcodebert-rdrop/checkpoint-1000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/b74eaa6f2792249a25994c9bdd704c51ea169454/graphcodebert-rdrop/checkpoint-1000/optimizer.pt
4.74 MB
- Xet hash:
- 51bfbf01aad6cd11ea2f373dacf7f3f5a324012fbdd73bb2823908d948ee8c7e
- Size of remote file:
- 4.74 MB
- SHA256:
- 6463d936c9004d9610dc37c3709533220325775d0fee173d8a65fa040c277349
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