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-300/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-300/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-300/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/2a2618e52c7420dadd88e2cb7b9b49e1aaa3a40e/checkpoint-300/training_args.bin
5.84 kB
- Xet hash:
- 6982e8d41cd0d98aa001a2cc3f20e92cbf79e6444486d26b245d6d00c129e125
- Size of remote file:
- 5.84 kB
- SHA256:
- 35d071116340aee07c195df38edc9c8a987c20a84c3af618845dff316a63c31a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.