Instructions to use jiekeshi/GraphCodeBERT-Adversarial-Finetuned-Clone-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jiekeshi/GraphCodeBERT-Adversarial-Finetuned-Clone-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="jiekeshi/GraphCodeBERT-Adversarial-Finetuned-Clone-Detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("jiekeshi/GraphCodeBERT-Adversarial-Finetuned-Clone-Detection") model = AutoModelForMaskedLM.from_pretrained("jiekeshi/GraphCodeBERT-Adversarial-Finetuned-Clone-Detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#3)
Verified signature- Adding `safetensors` variant of this model (d8c5776069ed1f341c14d31d996e3870d72a0b76) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>21d503f
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Create config.json (#2)
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Update README.md
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Rename adv_model.bin to pytorch_model.bin
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