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:
- 37da155014c06179d160b8088ac2cc94579926af72c40766e532d9a4db0beeb1
- Size of remote file:
- 5.78 kB
- SHA256:
- 0256e15114b0df6f165bb3e6d75f279805d6f125574ac927fca7a602f9449f4a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.