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 codebert-base-cross-entropy/checkpoint-4000/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 4.74 MB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/caed3284efcf0158a73d4482c8e16c0eb809cc30/codebert-base-cross-entropy/checkpoint-4000/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@caed3284efcf0158a73d4482c8e16c0eb809cc30/codebert-base-cross-entropy/checkpoint-4000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/caed3284efcf0158a73d4482c8e16c0eb809cc30/codebert-base-cross-entropy/checkpoint-4000/optimizer.pt
4.74 MB
- Xet hash:
- b460a61eac67bdfc7a5da1e8d1547053ce8c693469c69566eb15334ceb1d5d8b
- Size of remote file:
- 4.74 MB
- SHA256:
- f7c3f30249d8e1bfbfb962de7737378143dd95355634be8f01ab5d26730903e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.