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:
- 14fa85810b78d2175fa772b206e0c75b6d9179997ec40d769bca630341b8134e
- Size of remote file:
- 504 MB
- SHA256:
- 4eac14ed59a07a54ac4e696a827dbd07481c2c2e61a0b6fb79b373aa86d1ca27
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.