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
graphcodebert-code-classification / graphcodebert-base-lowLR-highBatchSize /checkpoint-550 /model.safetensors
Download graphcodebert-base-lowLR-highBatchSize/checkpoint-550/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-base-lowLR-highBatchSize/checkpoint-550/model.safetensors
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification/graphcodebert-base-lowLR-highBatchSize/checkpoint-550/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/main/graphcodebert-base-lowLR-highBatchSize/checkpoint-550/model.safetensors
499 MB
- Xet hash:
- 85fb5dc8a7ca15bb04dfab26c7357cbe884dbc7212a4f141d1c0a5e24a2f3a0f
- Size of remote file:
- 499 MB
- SHA256:
- 3522ebeb3e76886d055b63e7dbb614e25d4eb7eaa31fe407b0f6ce724a359f2f
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