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 graphcodebert-base-lowLR-highBatchSize/checkpoint-450/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/90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-450/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-450/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/90ab2a31ac83f4d8788a25576fbac2f0d0a9ffa7/graphcodebert-base-lowLR-highBatchSize/checkpoint-450/optimizer.pt
4.74 MB
- Xet hash:
- 5508632d1cb6167f6a347d69d5409ccd6e304da978915d103421ff9d85c85247
- Size of remote file:
- 4.74 MB
- SHA256:
- c5630152242afd0e6c6c4e2ab8452cd1ed56b7076e9b3c2c1648d7ec31e47aff
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