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:
- 60995ab5037c74a2a33c2ceed86c1e4e18a2c862b305270b68a58883d3a1a603
- Size of remote file:
- 14.6 kB
- SHA256:
- 89f0f406618b5197fae4c6d900acbd98b2b79bf347c744c857d3d77432494a61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.