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AIGC Detector Env1

Developed by yuchuantian
A multi-scale positive-unlabeled detector based on RoBERTa-Base pre-trained model for identifying AI-generated text
Downloads 48
Release Time : 12/26/2023

Model Overview

This model employs the Multi-scale Positive-Unlabeled detection method (MPU), specifically designed to detect AI-generated text content, trained on the HC3 English dataset.

Model Features

Multi-scale detection
Uses the Multi-scale Positive-Unlabeled detection method (MPU) to improve detection accuracy
RoBERTa-based
Utilizes the RoBERTa-Base pre-trained model as the foundational architecture
Specialized for AIGC
Specifically designed to detect AI-generated text content

Model Capabilities

AI-generated text detection
Text classification
Natural language processing

Use Cases

Content moderation
AI-generated content identification
Identify AI-generated content on platforms such as social media and forums
Effectively distinguishes between human-written and AI-generated text
Academic integrity
Academic paper detection
Detect potential AI-generated content in academic papers
Helps maintain academic integrity
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