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-1400/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/f6ef07acbf5e482d0551d53e2f8ba9d420e7fbb5/checkpoint-1400/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@f6ef07acbf5e482d0551d53e2f8ba9d420e7fbb5/checkpoint-1400/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/f6ef07acbf5e482d0551d53e2f8ba9d420e7fbb5/checkpoint-1400/optimizer.pt
1.01 GB
- Xet hash:
- 7accff7ee49e49e87e4b71d784d06be36f6823947955097ba778faf5253f0bd4
- Size of remote file:
- 1.01 GB
- SHA256:
- 43a57e6a7c228eb2f958c5c0f014adf5020d1599958af5881807940de25d2649
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