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-500 /model.safetensors
Download graphcodebert-base-lowLR-highBatchSize/checkpoint-500/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-500/model.safetensors
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification/graphcodebert-base-lowLR-highBatchSize/checkpoint-500/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/main/graphcodebert-base-lowLR-highBatchSize/checkpoint-500/model.safetensors
499 MB
- Xet hash:
- 4a0eb03799017faa9445a52fa4cbc796fb16f73a897c5ca7922dff5c80a69f5b
- Size of remote file:
- 499 MB
- SHA256:
- 62afd78fff103664d60d34ba3d06f2e7b451350dbc1f5f43dca6b0c42a813f0a
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