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-1500/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.01 GB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1500/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/caed3284efcf0158a73d4482c8e16c0eb809cc30/checkpoint-1500/optimizer.pt
1.01 GB
- Xet hash:
- af3b88d8d4264f0e75d51f5442bc4637d5f6df8699d9bf69e0948a6efbd3c374
- Size of remote file:
- 1.01 GB
- SHA256:
- d198b2916f97edec4db8f6e6e1de0fbe2238e1e5f87ecbe27e73cd142a9bec33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.