Instructions to use AMindToThink/code-detection-confound-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMindToThink/code-detection-confound-checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AMindToThink/code-detection-confound-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download java_raw_ce/model.bin from AMindToThink/code-detection-confound-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/AMindToThink/code-detection-confound-checkpoints/resolve/e480c40fce869e48412e269836f05cadbebae9a3/java_raw_ce/model.bin
- Command line
-
hf download hf://AMindToThink/code-detection-confound-checkpoints@e480c40fce869e48412e269836f05cadbebae9a3/java_raw_ce/model.bin
-
curl -L -o model.bin https://huggingface.co/AMindToThink/code-detection-confound-checkpoints/resolve/e480c40fce869e48412e269836f05cadbebae9a3/java_raw_ce/model.bin
504 MB
- Xet hash:
- 0d3ddd7ce858923ea51dcb01f1e30f4be2322410bd7d31ffa599f50af3b0e72a
- Size of remote file:
- 504 MB
- SHA256:
- 0894753313c0c731896f197944439f8de050728c3b8ac7e65a8fd3e3f8065cee
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