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-base-lowLR-highBatchSize /final_model /model.safetensors
- Xet hash:
- 8780e1bc4c965931e2631732dd3f7117a3be8c2a514cac69128d0f0af47a8bbc
- Size of remote file:
- 499 MB
- SHA256:
- 007997eaa459b87c8c3017ef28539ea952197e645a14c6e2fe3bb37f8d4ede5c
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