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-900 /model.safetensors
- Xet hash:
- 132dc1df78a8a211fc6a1e1396cc5fdd43d14c2b81a9859bb74fe863771fd90f
- Size of remote file:
- 499 MB
- SHA256:
- a2b4b28e3f41b50f1e79a215c3717c6cdbb00864d1d19a89b49453800d3a6498
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