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-100/scheduler.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.47 kB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-mixcode-fft/checkpoint-100/scheduler.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-mixcode-fft/checkpoint-100/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-mixcode-fft/checkpoint-100/scheduler.pt
1.47 kB
- Xet hash:
- d7ac30c9d8e52e11d9f6553dffdb0a1465748f372d5f4abb2cb84b6b8bd5d6ed
- Size of remote file:
- 1.47 kB
- SHA256:
- 73cb732442e2d829e594b5b61d500d80c02db855b5b504235793fe2f865f5954
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.