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/9c7b3d30d8d7b8a37704b2952b8345afa3d163f8/checkpoint-1000/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@9c7b3d30d8d7b8a37704b2952b8345afa3d163f8/checkpoint-1000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/9c7b3d30d8d7b8a37704b2952b8345afa3d163f8/checkpoint-1000/optimizer.pt
1.01 GB
- Xet hash:
- c784ed04571516f0b9a098a118f459c404bc15f0bc0e0da27ca7b636979ce1dc
- Size of remote file:
- 1.01 GB
- SHA256:
- cce9d35e3077c44ed259aba54f709f632f5bf67a59fa794ed03bf3be1f17490a
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