Instructions to use johnson/AI-writing-detector-laya-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Laya
How to use johnson/AI-writing-detector-laya-zh with Laya:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
AI-writing-detector-laya-zh
基于 Laya(421M 多语 System One 决策模型)微调的中文论文句级 AI 写作检测器: 判断一句话是人工撰写、AI 轻度润色,还是 AI 改写。
- 基座:
convaiinnovations/laya多语检查点(ModernBERT-large,421M) - 训练:9.99 万句中文论文句子,1 epoch,RLCD 校准
- 标签:
human/deepseek_light/deepseek_heavy - 许可:Apache-2.0
- 数据:johnson/AI-writing-detector-dataset
性能
| 指标 | 数值 |
|---|---|
| TPR@1%FPR | 0.8954 |
| AUC | 0.9935 |
| FPR@1% | 1.03% |
对照:RoBERTa-wwm-ext 102M 为 0.831。
用法
pip install laya huggingface_hub
import json
from huggingface_hub import snapshot_download
import laya
d = snapshot_download("johnson/AI-writing-detector-laya-zh")
agent = laya.load(d)
questions = json.load(open(f"{d}/questions.json"))
ans = agent.predict("此外,所提出的谱特征选择算法……", questions)["answers"]["label"]
probs = ans["probabilities"]
p_ai = probs["deepseek_light"] + probs["deepseek_heavy"]
print(ans["choice"], round(p_ai, 3))
命令行:
python inference.py --input paper.txt --thr 0.9
默认阈值 0.9(≈1% FPR),高精确场景用 0.95。
训练配置
10 万句、1 epoch、micro-batch 8 × grad-accum 8、max_len 128、encoder lr 2.5e-5、A800 约 30 分钟。
限制
- 只验证了中文学术;医学/英文同配置下不如小模型;
- 只覆盖 DeepSeek 系改写;单句、无上下文;
- 存在误报,不得用于学术不端判定。
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