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-vanilla/checkpoint-200/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/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-vanilla/checkpoint-200/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-vanilla/checkpoint-200/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-vanilla/checkpoint-200/training_args.bin
5.91 kB
- Xet hash:
- 24fb21546084cfb8d45113042f9f07b3a922d4d7b9c64c152a6fa95c112b454b
- Size of remote file:
- 5.91 kB
- SHA256:
- a3d448040cc8193c39a3aa43e3972db0566234234cb8950ae351de6781f2f556
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