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-800/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/2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-800/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-800/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-800/optimizer.pt
4.74 MB
- Xet hash:
- 2e3505e2d31bff1e3903ed4c8ec9d480b2051561911aaec90342ed053a3c048b
- Size of remote file:
- 4.74 MB
- SHA256:
- 77eed31466f224a3ed4dccdd0233a5e16ed17aae4a8afe7d35bd17f70ab96e11
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