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:
- 572bd84a0fb99978481b9f70eb78a125103aef270162f762af7bbc6ac780c8df
- Size of remote file:
- 499 MB
- SHA256:
- ac86433275c4c5f90dffc8271cd1029e8abe70a5522abf0e31073eec45c4cc88
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.