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-base-lowLR-highBatchSize/checkpoint-350/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/90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-350/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-350/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-350/optimizer.pt
4.74 MB
- Xet hash:
- 22154cd190e69c52355679144d6202f1dd0328d28912bda46829c8a5fa968358
- Size of remote file:
- 4.74 MB
- SHA256:
- fc46c104c26de9b07a90c7a38f083f5e2a8d00cb7be0e639f6f74084fd942547
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