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  ---
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  language:
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- - en
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- thumbnail: "url to a thumbnail used in social sharing"
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  tags:
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- - ArguGPT
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  license: mit
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  datasets:
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- - ArguGPT
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  metrics:
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- - accuracy
 
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  ---
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  # ArguGPT
@@ -19,3 +20,18 @@ RoBERTa-large finetuned on ArguGPT essays.
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  - label 0 for human written essays
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  **Please truncate your input essay to 512 tokens**
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  language:
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+ - en
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+ thumbnail: url to a thumbnail used in social sharing
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  tags:
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+ - ArguGPT
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  license: mit
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  datasets:
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+ - ArguGPT
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  metrics:
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+ - accuracy
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+ pipeline_tag: text-classification
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  ---
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  # ArguGPT
 
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  - label 0 for human written essays
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  **Please truncate your input essay to 512 tokens**
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+
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+ ## Citation
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+
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+ Please cite our work [arXiv:2304.07666](https://arxiv.org/abs/2304.07666) as
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+
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+ ```
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+ @misc{liu2023argugpt,
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+ title={ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models},
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+ author={Yikang Liu and Ziyin Zhang and Wanyang Zhang and Shisen Yue and Xiaojing Zhao and Xinyuan Cheng and Yiwen Zhang and Hai Hu},
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+ year={2023},
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+ eprint={2304.07666},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```