🚀Installation
-
Clone the repositories:
git clone https://github.com/zjunlp/ChineseHarm-bench cd ChineseHarm-bench git clone https://github.com/hiyouga/LLaMA-Factory -
Install dependencies:
cd LLaMA-Factory pip install -e ".[torch,metrics]"
📚Inference
We release the following variants of our harmful content detection model:
🔹 Single Inference (Example)
Run single-input inference using the ChineseGuard-7B model:
SCRIPT_PATH="../infer/single_infer.py"
model_name="zjunlp/ChineseGuard-7B"
text="代发短信，有想做的联系我，无押金"
python $SCRIPT_PATH \
--model_name $model_name \
--text $text
🔸 Batch Inference (Multi-NPU or Multi-GPU)
To run inference on the entire ChineseHarm-Bench using ChineseGuard-7B and 8 NPUs:
SCRIPT_PATH="../infer/batch_infer.py"
model_name="zjunlp/ChineseHarm-7B"
file_name="../benchmark/bench.json"
output_file="../benchmark/bench_ChineseHarm-7B.json"
python $SCRIPT_PATH \
--model_name $model_name \
--file_name $file_name \
--output_file $output_file \
--num_npus 8
For more configuration options (e.g., batch size, device selection, custom prompt templates), please refer to
single_infer.pyandbatch_infer.py.Note: The inference scripts support both NPU and GPU devices.
🚩Citation
Please cite our repository if you use ChineseGuard in your work. Thanks!
@misc{liu2025chineseharmbenchchineseharmfulcontent,
title={ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark},
author={Kangwei Liu and Siyuan Cheng and Bozhong Tian and Xiaozhuan Liang and Yuyang Yin and Meng Han and Ningyu Zhang and Bryan Hooi and Xi Chen and Shumin Deng},
year={2025},
eprint={2506.10960},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.10960},
}