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-1000/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/0054bd3ed5a4f97669870fe39d23019e9ff9edbc/checkpoint-1000/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@0054bd3ed5a4f97669870fe39d23019e9ff9edbc/checkpoint-1000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/0054bd3ed5a4f97669870fe39d23019e9ff9edbc/checkpoint-1000/optimizer.pt
1.01 GB
- Xet hash:
- 4a7d01be719515b62ddd9dcc91ccd4b078b25a42ec44af156af15bec0197b78a
- Size of remote file:
- 1.01 GB
- SHA256:
- 8e5bd83a18fef7f081ebc6b4706dc3434cc22b7353d2021ca7d668463d93dc46
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