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:
- f7a18d39e2671e30911f0e27755c3cc44565d2c04e801798e56261a162630560
- Size of remote file:
- 5.84 kB
- SHA256:
- b5fc4023adc281644437a690ea6a6001846d7762699cd428d4ad38e1888076db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.