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:
- cbd343f8c1b0facee5a130f729853d549d686ea3649483e833117a8cd8a9bf35
- Size of remote file:
- 14.6 kB
- SHA256:
- b99b7722392a9306ca3170ca521fd41cb63a608be5ab5fddc849d9be0c714edc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.