AnxForever/chinese-ai-detector-bert

Chinese AI-Generated Text Detector — BERT v11c (Boundary-Fix)

中文 AI 生成文本检测器（本科毕业设计最终版） A fine-tuned BERT model that classifies Chinese text as either human-written (0) or AI-generated (1). The main released model is a document-level binary classifier; mixed-text boundary detection is an experimental extension provided by a separate span model.

📌 模型概述 / Overview

中文：本模型是基于 bert-base-chinese 微调的中文 AI 生成文本二分类器，为本科毕业设计「基于 BERT 微调的中文 AI 生成文本检测系统」的最终生产模型（v11c boundary-fix 版本）。当前主链路输出 Human / AI 二分类；[SEP] 边界标记与 Token 级 span detector 是配套的实验性扩展，用于探索构造型人机混写样本中的片段级分析。

English: A binary classifier fine-tuned on bert-base-chinese for Chinese AI-generated text detection. This is the final production checkpoint (v11c boundary-fix) of an undergraduate thesis project. [SEP] boundary markers and the token-level span detector are experimental extensions for constructed human/AI mixed-text analysis, not the default production inference path.

📊 评估指标 / Evaluation

The metrics above evaluate the document-level binary classifier. The historical token-level boundary result belongs to the separate chinese-ai-detector-span experimental model and should not be mixed with the main classifier metrics.

Independent eval by source (selected)

🏗️ 架构 / Architecture

• —Base model: bert-base-chinese (12 layers, hidden 768, 12 heads, vocab 21,128)
• —Head: BertForSequenceClassification (2 labels: 0 = human, 1 = AI)
• —Max sequence length: 256 tokens (train), 512 (supported)
• —Framework: transformers 4.57.3, PyTorch 2.0+
• —Parameters: ~102M

Training configuration

Data changes vs. v10 baseline

• —Removed 750 hard patterns + 1,767 unapproved samples + 7 length violations
• —Added 300 formal-collected weak-domain samples
• —Added 300 Llama-405B weak-domain samples
• —Added 2,131 long-AI boundary-fix samples (the key v11c contribution)
• —Net change: +207 rows vs. v10

🚀 使用方法 / Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

MODEL_ID = "AnxForever/chinese-ai-detector-bert"
TEMPERATURE = 0.8165  # Temperature scaling, calibrated on 910 samples (ECE=0.0034)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
model.eval()

text = "这是一段需要检测的中文文本。"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)

with torch.no_grad():
    logits = model(**inputs).logits
    # Apply temperature scaling for calibrated confidence
    probs = torch.softmax(logits / TEMPERATURE, dim=-1)[0]

pred_idx = int(probs.argmax())
label = model.config.id2label[pred_idx]   # "human-written" or "AI-generated"
print(f"{label}  (confidence: {probs[pred_idx].item():.2%})")

Label mapping

• —0 → human-written (人类撰写)
• —1 → AI-generated (AI 生成)

Note on Temperature Scaling: T = 0.8165 was calibrated on a held-out 910-sample set and brings ECE from 0.0121 down to 0.0034. For uncalibrated probabilities, set TEMPERATURE = 1.0.

🎯 技术贡献 / Contributions

1. 1.Data-centric risk governance The v11c model keeps the BERT backbone fixed and improves robustness through data cleaning, weak-domain supplementation, long-AI supplementation, and calibrated inference.

1. 1.`[SEP]` boundary-marker experiment In constructed C2-style mixed samples, [SEP] was used as an explicit boundary hint between known human and AI segments. This is an engineering experiment for mixed-text modeling, not a claim that [SEP] itself can identify authorship without labels.

1. 1.Two-stage experimental extension
2. 2.Stage 1: this model — document-level Human / AI classification
3. 3.Stage 2: separate span detector — token-level Human / AI tagging on mixed-text samples
4. 4.See `AnxForever/chinese-ai-detector-span`

1. 1.Long-AI boundary-fix (v11c) 针对长 AI 段落在边界处易被误判的问题，补充 2,131 条长 AI 边界样本，使 256+ token 桶的准确率恢复到 V10 水平。

Note on mixed-text boundary detection

The boundary module was trained on a relatively small constructed mixed-text set. It is useful for demonstration, teaching, and secondary development, but it should be treated as an experimental prototype. For real business scenarios, mixed human/AI data from the target domain should be collected, labeled, retrained, and evaluated before deployment.

⚠️ 局限性 / Limitations

• —仅针对中文文本；对英文或其他语言无保证。
• —训练语料偏新闻/百科/技术/正式文体，对诗歌、古文、社交媒体短文本可能欠拟合。
• —当前默认发布能力是篇章级二分类；人机混写边界定位属于实验性扩展，不建议直接作为商业审核结论。
• —训练数据主要来自 DeepSeek、Gemini、GPT、Llama-405B 等主流模型；对经过重度改写的 AI 文本仍有遗漏风险。
• —对短文本、强人工改写文本、多次交替混写文本和目标域外文本，不保证固定准确率。

🗂️ 相关资源 / Related

• —📊 训练数据集 / Dataset: `AnxForever/chinese-ai-detection-dataset`
• —🎯 边界检测器 / Span detector: `AnxForever/chinese-ai-detector-span`

📜 License

MIT License Copyright (c) 2026 AnxForever

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

✍️ Citation

@misc{anxforever2026chineseaidetectorbert,
  title  = {Chinese AI-Generated Text Detector with Boundary Markers (BERT v11c)},
  author = {AnxForever},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/AnxForever/chinese-ai-detector-bert}},
  note   = {Undergraduate thesis project}
}