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 /checkpoint-500 /model.safetensors
- Xet hash:
- 4a0eb03799017faa9445a52fa4cbc796fb16f73a897c5ca7922dff5c80a69f5b
- Size of remote file:
- 499 MB
- SHA256:
- 62afd78fff103664d60d34ba3d06f2e7b451350dbc1f5f43dca6b0c42a813f0a
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