ai text detector v1.01 EU AI Act Compliance Profile

desklib/ai-text-detector-v1.01

Overview

This AI-generated text detection model, developed by Desklib, is designed to classify English text as either human-written or AI-generated. It currently leads the RAID Benchmark for AI Detection. This model is a fine-tuned version of microsoft/deberta-v3-large, leveraging a transformer-based architecture to achieve high accuracy. It is robust and handles various adversarial attacks across different domains remarkably well. This model is particularly useful for applications in content moderation, academic integrity, journalism, and anywhere the authenticity of text is paramount.

Desklib provides AI-based tools for personalized learning and study help. This model is one of the many tools offered by Desklib for students, educators and universities.

Try the model online!: Desklib AI Detector

Github Repo: https://github.com/desklib/ai-text-detector

Performance

This model achieves top performance on the RAID benchmark at the time of submission: Visit RAID Leaderboard

Model Architecture

The model is built upon a fine-tuned microsoft/deberta-v3-large transformer architecture. The core components include:

• Transformer Base: The pre-trained microsoft/deberta-v3-large model serves as the foundation. This model utilizes DeBERTa (Decoding-enhanced BERT with disentangled attention), an improved version of BERT and RoBERTa, which incorporates disentangled attention and enhanced mask decoder for better performance.
• Mean Pooling: A mean pooling layer aggregates the hidden states from the transformer, creating a fixed-size representation of the input text. This method averages the token embeddings, weighted by the attentio

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Base Model