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:
- c907b28651d9074a560d22ad92e2d17cc3cc4623bf2e018aa5b77e508941f4e8
- Size of remote file:
- 14.6 kB
- SHA256:
- 8dada50b8e3720c3fcc78ad23defc5018ee9745c3cb4d75d216a9d11d339cd84
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.