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-1300/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/caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1300/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1300/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1300/optimizer.pt
1.01 GB
- Xet hash:
- 1163c9ec9680010cf713ca1e4a1b3fe8c59763a69a8e416556789cd244817f6d
- Size of remote file:
- 1.01 GB
- SHA256:
- 9e861dbcff7c0be667b3b075ca5969aa371617888a7ee45021c678aeffa0453b
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