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-800/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-800/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@b0447775581a3939d2778bd4499e73c852cebb42/taskA-codebert-base-focal/checkpoint-800/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/b0447775581a3939d2778bd4499e73c852cebb42/taskA-codebert-base-focal/checkpoint-800/optimizer.pt
995 MB
- Xet hash:
- 19babbc0292ab031d6e2783b09b249629e878436f4bff090f4f42287036c8d24
- Size of remote file:
- 995 MB
- SHA256:
- 84e12c96f22c50f662c931eaa3a5541ca7e327e5bdfc79de81b6efd0648568c0
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