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:
- fbf44bb9ed62853e58debfa9ad76fd9e9ced13617de5111a0da258923153ce6d
- Size of remote file:
- 1.47 kB
- SHA256:
- f8e119b4cd3f8598a5af51d58c37b3dbd9b0034a064c6718afecaa6a4b129a23
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.