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:
- a8fba59b5d9db9fe31ef017865c48e60ed40cf4fda84f2409f91f3e998006f75
- Size of remote file:
- 1.47 kB
- SHA256:
- 3263b141dfd7a7e0528f242a4444359047629a6da5cdc390920609ed07597b9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.