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
- Xet hash:
- c784ed04571516f0b9a098a118f459c404bc15f0bc0e0da27ca7b636979ce1dc
- Size of remote file:
- 1.01 GB
- SHA256:
- cce9d35e3077c44ed259aba54f709f632f5bf67a59fa794ed03bf3be1f17490a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.