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-1022/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/647ce03e76666d3b3bde9deea2ed5af326857baf/graphcodebert-base-lowLR-highBatchSize/checkpoint-1022/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@647ce03e76666d3b3bde9deea2ed5af326857baf/graphcodebert-base-lowLR-highBatchSize/checkpoint-1022/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/647ce03e76666d3b3bde9deea2ed5af326857baf/graphcodebert-base-lowLR-highBatchSize/checkpoint-1022/optimizer.pt
4.74 MB
- Xet hash:
- 03f03d77768f9be5b9cdcc1614b54d9ccda7353035030fc26d2676f7b113d87a
- Size of remote file:
- 4.74 MB
- SHA256:
- 14570faac475f55376f6ac26bdbbf15b3ee3a6f9abcd17d2521f8e05a2c814a0
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