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:
- d812171430486dad79c9c13b8ff2fbda2292b8bdf13a1bb48a1aef074bf2e5df
- Size of remote file:
- 1.47 kB
- SHA256:
- f3347c274cf8389fea5bc6a6e8736d647cc7704359db22052e77f18e8ed9d685
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.