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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2000/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.01 GB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/218aef1c8c3bdf2f70ee1c2818bd793f99758f8d/checkpoint-2000/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@218aef1c8c3bdf2f70ee1c2818bd793f99758f8d/checkpoint-2000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/218aef1c8c3bdf2f70ee1c2818bd793f99758f8d/checkpoint-2000/optimizer.pt
1.01 GB
- Xet hash:
- e5c43b31a2c00310e11e55e3b2e5329ee056d626cf9380d93fa267916d299849
- Size of remote file:
- 1.01 GB
- SHA256:
- de381cf7f4ef9473b9b1b5f342e27792358d8224775d528b2d8711776058a115
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.