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:
- 4c23ea98d267980e6b7ce818b5854f5b90ab9baf5d7fac91189c9574f1f9dba4
- Size of remote file:
- 4.74 MB
- SHA256:
- 87435a9abbaae7469cc5c4ac6d12b641e525d161077ffaade93c7601b2ba4e06
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.