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 taskA-codebert-base-focal/checkpoint-600/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 995 MB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/b0447775581a3939d2778bd4499e73c852cebb42/taskA-codebert-base-focal/checkpoint-600/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@b0447775581a3939d2778bd4499e73c852cebb42/taskA-codebert-base-focal/checkpoint-600/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/b0447775581a3939d2778bd4499e73c852cebb42/taskA-codebert-base-focal/checkpoint-600/optimizer.pt
995 MB
- Xet hash:
- d3d99b617dc61f3a30e13b2e9f9aea2b3ddefc1561a36c3bd7450ac464e5fde1
- Size of remote file:
- 995 MB
- SHA256:
- 879a9b07c456c6dca73868fa2e3278d0bb5aaaf47d2613f8ac75871f309b0174
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