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
graphcodebert-code-classification / graphcodebert-base-lowLR-highBatchSize /checkpoint-1000 /training_args.bin
Download graphcodebert-base-lowLR-highBatchSize/checkpoint-1000/training_args.bin from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-1000/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-1000/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-1000/training_args.bin
5.91 kB
- Xet hash:
- 3fe011866abee72f1469bb40ab2e8d7633229765330454957f5484755400250e
- Size of remote file:
- 5.91 kB
- SHA256:
- a8307116b3e23cecae8ec65819b3c2e7993d8af193c50142730ae3c50bce70d6
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