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:
- bce077b4178593e7cbe086f9b283a286ca162ef144dca14d185914c64b80d48a
- Size of remote file:
- 4.74 MB
- SHA256:
- b4b2aca589412ab916678ce8d9e00aeb607955c6f721e38f8415fd21948a1c92
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.