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:
- 8a91780636237f78b308c2b7deb7a12cc343707040db3ce978a90187fa893124
- Size of remote file:
- 1.98 kB
- SHA256:
- 7d6df5182cd30783493b8bfe8da6f86053d35f68253871d1325714d40751c739
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.