
## Citation

Please cite our work [arXiv:2304.07666](https://arxiv.org/abs/2304.07666) as  

```
@misc{liu2023argugpt,
      title={ArguGPT: evaluating, understanding and identifying argumentative essays generated by GPT models}, 
      author={Yikang Liu and Ziyin Zhang and Wanyang Zhang and Shisen Yue and Xiaojing Zhao and Xinyuan Cheng and Yiwen Zhang and Hai Hu},
      year={2023},
      eprint={2304.07666},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
{"language": ["en"], "tags": ["AIGC for education"], "datasets": ["SJTU-CL/ArguGPT"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"}
transformers pytorch safetensors roberta text-classification AIGC for education en dataset:SJTU-CL/ArguGPT arxiv:2304.07666 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #AIGC for education #en #dataset-SJTU-CL/ArguGPT #arxiv-2304.07666 #autotrain_compatible #endpoints_compatible #has_space #region-us 


Please cite our work arXiv:2304.07666 as  


TAGS
#transformers #pytorch #safetensors #roberta #text-classification #AIGC for education #en #dataset-SJTU-CL/ArguGPT #arxiv-2304.07666 #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #safetensors #roberta #text-classification #AIGC for education #en #dataset-SJTU-CL/ArguGPT #arxiv-2304.07666 #autotrain_compatible #endpoints_compatible #has_space #region-us 
