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-mixcode-fft/checkpoint-200/model.safetensors from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/7343966b3865449dee363053dd0adf7300c19484/graphcodebert-mixcode-fft/checkpoint-200/model.safetensors
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@7343966b3865449dee363053dd0adf7300c19484/graphcodebert-mixcode-fft/checkpoint-200/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/7343966b3865449dee363053dd0adf7300c19484/graphcodebert-mixcode-fft/checkpoint-200/model.safetensors
499 MB
- Xet hash:
- caba5b3a2714a449ce697e8e2494a236ab7d24dcc94dcf3fe7edde9a3fdea7a3
- Size of remote file:
- 499 MB
- SHA256:
- 9c44ac861ac2b8fa337d7fb36dbec3be1474c43bf7e3cd7ab7daad107f775dc3
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