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:
- 2abfa0ff788fde5549b2c1867931651991617e02c46a4c5febe651f1d857a04d
- Size of remote file:
- 499 MB
- SHA256:
- 144222bcc915ff7437db4cd2da088235b9e86e1b0e69f1ead95746d2ce8331be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.