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-400/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/2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-400/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-400/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-400/optimizer.pt
1.01 GB
- Xet hash:
- 3fe5dc768fe9714b604e00feebd46dca2dbbcb33e0c521b60eec39e4eb597611
- Size of remote file:
- 1.01 GB
- SHA256:
- d82bb9793c8d840a9948fe203133398c8ec366eb90d59c0eae5af63cf790fe2b
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