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:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download graphcodebert-vanilla/checkpoint-100/model.safetensors from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/main/graphcodebert-vanilla/checkpoint-100/model.safetensors
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification/graphcodebert-vanilla/checkpoint-100/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/main/graphcodebert-vanilla/checkpoint-100/model.safetensors
499 MB
- Xet hash:
- db87a81f0f8e280cf5e5067b68f14a4992b06655115cce01d1c6212bf991a61c
- Size of remote file:
- 499 MB
- SHA256:
- da3f281851fd71b6943f4e6fc58ef17ba54c6d167a319cabac7deab1eafcd599
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.