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:
- 21e215f821668001cce8612f3a40a4afc86adcda0d4278f99c0fc29d658fdd5c
- Size of remote file:
- 1.38 kB
- SHA256:
- c163c50c228653200a6e5e8cf09cac438d40ad4c6e887fb4c11f937ce9ea51c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.