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-1200/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/caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1200/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1200/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1200/optimizer.pt
4.74 MB
- Xet hash:
- 89dcabc560cdacc26b9f1d5b1f3d99baef21352a0e1d947adb2077f9655a9c1c
- Size of remote file:
- 4.74 MB
- SHA256:
- 91d4f9219105610d445d1323ff323318ea75bcd29155e0cbd304baef2009c888
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