File size: 2,038 Bytes
99c11c6 2fcb21e de153a2 99c11c6 5154e62 99c11c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
license: cc-by-nc-4.0
language:
- zh
pipeline_tag: text-classification
datasets:
- zjunlp/ChineseHarm-bench
---
## 🚀Installation
1. Clone the repositories:
```bash
git clone https://github.com/zjunlp/ChineseHarm-bench
cd ChineseHarm-bench
git clone https://github.com/hiyouga/LLaMA-Factory
```
2. Install dependencies:
```bash
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-3B model:
```
SCRIPT_PATH="../infer/single_infer.py"
model_name="zjunlp/ChineseGuard-3B"
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-3B and 8 NPUs:
```
SCRIPT_PATH="../infer/batch_infer.py"
model_name="zjunlp/ChineseHarm-3B"
file_name="../benchmark/bench.json"
output_file="../benchmark/bench_ChineseHarm-3B.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.py` and `batch_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!
```bibtex
@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},
}
``` |