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-mixcode-fft/checkpoint-50/rng_state.pth from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 14.6 kB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-50/rng_state.pth
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-50/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/4f8c4ed3d9cf9cdc213702412916e71be9634c88/graphcodebert-mixcode-fft/checkpoint-50/rng_state.pth
14.6 kB
- Xet hash:
- 3c3fa2267ea65603fe1b05cef876851c2d62c32990f6ead0e0b276cb166ceec0
- Size of remote file:
- 14.6 kB
- SHA256:
- 08caf38b13d45258389b41019ea11e8ec28ca9257b10c167e0da2af4ab8e3ec5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.