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:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
graphcodebert-code-classification / graphcodebert-swa-from-epoch-1 /checkpoint-1300 /model.safetensors
- Xet hash:
- 2231909a9f8221d91370431553cdb4ba26ebde6768d11a6438b8415bf1898316
- Size of remote file:
- 499 MB
- SHA256:
- 765035226f2c33b7f7c9c48302463744e4e2b9f073e8acc72d38881d5a154262
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