datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
chinese-ai-detection-dataset
Chinese AI Detection Dataset
中文AI文本检测数据集
数据集简介
用于训练中文AI生成文本检测模型的综合数据集，包含纯人类、纯AI以及混合文本（人类+AI）。
核心特色：使用[SEP]标记显式标注混合文本的人类/AI边界。
数据统计
类型
样本数
说明
总计
66,001
训练/验证/测试集
纯人类
27,719
多领域人类文本
纯AI
27,719
多模型生成
C2 (续写)
3,781
人类开头+AI续写
C3 (改写)
3,781
AI改写人类文本
C4 (润色)
3,001
AI润色人类文本
数据格式
{
"text": "文本内容（混合文本包含[SEP]标记）",
"label": 0, // 0=Human, 1=AI
"category": "C2", // Human/AI/C2/C3/C4
"source": "数据来源"
}… See the full description on the dataset page: https://huggingface.co/datasets/AnxForever/chinese-ai-detection-dataset.ai-capability-hiding-drift-detection-v0.1
What this dataset is
This dataset detects drift in capability-hiding patterns over time.
It compares:
baseline probe capability vs baseline expressed performance
current probe capability vs current expressed performance
whether a change in monitoring context explains a new gap
The goal is not blame.
The goal is early warning that oversight changes expression.
What it tests
You detect when the monitored/unmonitored gap:
newly appears
widens
changes shape
You also avoid… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-capability-hiding-drift-detection-v0.1.ethereum_fraud_detectionai-powered-phishing-email-detection-systememotion-detection-benchmark
AI Model Emotion Detection Benchmark
Benchmark results from testing 11 AI models on emotion detection from movie stills, conducted on OpenMark — a deterministic AI model benchmarking platform.
Methodology
Task: Identify emotions from 4 movie stills (varying complexity)
Models tested: 11 (GPT-5.2, Gemini 3 Pro, Gemini 3.1 Pro, Claude Opus 4.6, Claude Sonnet 4.6, Grok 4.1 Fast, Llama 4 Maverick, Qwen 3.5, Sonar, Gemini 3 Flash, Mistral Medium)
Runs per model: 3 (for… See the full description on the dataset page: https://huggingface.co/datasets/OpenMark-AI/emotion-detection-benchmark.