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:
- a919aca892e1312ca024b9b93852c04b230e4698b5bf1f20c1656af1678a60c4
- Size of remote file:
- 1.98 kB
- SHA256:
- 0690ce65a5c08741822b66ea60503c9fbb2e04d3b8bd9a51620fb356f773ab32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.