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 codebert-base-cross-entropy/checkpoint-3800/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/8cafee4a0eb534e7269daea77fadcd7cfbc89910/codebert-base-cross-entropy/checkpoint-3800/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@8cafee4a0eb534e7269daea77fadcd7cfbc89910/codebert-base-cross-entropy/checkpoint-3800/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/8cafee4a0eb534e7269daea77fadcd7cfbc89910/codebert-base-cross-entropy/checkpoint-3800/optimizer.pt
4.74 MB
- Xet hash:
- a30d8e97ed6ee87816fb14c8e5c9530a8e5e5282445e4b932737552354fdd460
- Size of remote file:
- 4.74 MB
- SHA256:
- e82f4498e7198e81b562310f4ff40c7971d53a8a84bff0b71e58281976613451
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