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
Commit History
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upload graphcodebert base, more training = more assign to majority class, poor f1 score T_T
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upload model weights after 2400 training steps with frozen pretrained weights, this performs worse than checkpoint 1000 on test set T_T
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upload model weights after 2400 training steps with frozen pretrained weights, this performs worse than checkpoint 1000 on test set T_T
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upload model weights after 1200 training steps with frozen pretrained weights
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