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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f96a6105b0967207aaccc5d539b0a0abcba845f56db7a4efdd0b37e57a0fcc91
- Size of remote file:
- 503 MB
- SHA256:
- 3990844c1aef2681b0a24e142ed3e13ef0ea070b005d6909d00f92d7eb00a0be
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