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-rdrop/checkpoint-600/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/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-rdrop/checkpoint-600/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-rdrop/checkpoint-600/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-rdrop/checkpoint-600/training_args.bin
5.84 kB
- Xet hash:
- eeb181f4f5056995aa11fc7f8019963704eeac717f52e19aca4e081ff35a87c1
- Size of remote file:
- 5.84 kB
- SHA256:
- e7cd390fef915efa37cfafbe6f2d5d365158faf1073058ef1c9ea09cda16a6a5
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