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-robust/checkpoint-200/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/26476e0af80ba0a4d10bb107e7ddbfeef1d6b225/graphcodebert-robust/checkpoint-200/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@26476e0af80ba0a4d10bb107e7ddbfeef1d6b225/graphcodebert-robust/checkpoint-200/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/26476e0af80ba0a4d10bb107e7ddbfeef1d6b225/graphcodebert-robust/checkpoint-200/optimizer.pt
4.74 MB
- Xet hash:
- c4a7afedbf700151dd98e9d739ae44f890aa222914fc26cc50ee958f22e392e9
- Size of remote file:
- 4.74 MB
- SHA256:
- 94ced15c772e225b8afaaa561ce73077f5f491b910b543982886ee79b2be71c0
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