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 graphcodebert-focal/checkpoint-200/training_args.bin from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/aae99447c446c0068d4f2a3c9f35c98d4c85b105/graphcodebert-focal/checkpoint-200/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@aae99447c446c0068d4f2a3c9f35c98d4c85b105/graphcodebert-focal/checkpoint-200/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/aae99447c446c0068d4f2a3c9f35c98d4c85b105/graphcodebert-focal/checkpoint-200/training_args.bin
5.84 kB
- Xet hash:
- 2141bed61cf92a40a67bd9840e2d7918d18a65e9adeffff373bb3ff2b5008c8e
- Size of remote file:
- 5.84 kB
- SHA256:
- 145afc62f33e66235dc7e780d87723128ed03df9823c7e83b2b2a20bc180074f
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