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 checkpoint-900/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/2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-900/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-900/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-900/training_args.bin
5.84 kB
- Xet hash:
- c642f10860b0563373637eb611027fae53903a9e83d120c9e2fc4026a3ef2961
- Size of remote file:
- 5.84 kB
- SHA256:
- efc150042e1ecc5adda0d12156f00c0a2be05d5a67f28124eb8b9f922305ebc0
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