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:
- 0a6fe5243980876b0dfa83578ddcca71a459958654dfebb1d8b6cbee91494fdb
- Size of remote file:
- 1.38 kB
- SHA256:
- 94c541eb6ef896f3e760a51d1437355e15c9e0b1b5365bcda1fa18a405d0bb55
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.