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 unixcoder-base/checkpoint-50/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 4.74 MB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/e0919c74a512d967ac67367da08a36ec8d87a765/unixcoder-base/checkpoint-50/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@e0919c74a512d967ac67367da08a36ec8d87a765/unixcoder-base/checkpoint-50/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/e0919c74a512d967ac67367da08a36ec8d87a765/unixcoder-base/checkpoint-50/optimizer.pt
4.74 MB
- Xet hash:
- 9c8d737d56e3e0e0542c69936f29100b8c9a618f990401c17a84092a53bb5f77
- Size of remote file:
- 4.74 MB
- SHA256:
- 18084ee24aa7c3f530476c469a3a46d151546383116de30be055e61fdb72b1f9
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