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GPT-2 Output Detector is an online demo of a machine learning model designed to detect the authenticity of text inputs. It is based on the RoBERTa model developed by HuggingFace and OpenAI and is implemented using the /Transformers library.. The demo allows users to enter text into a text box and receive a prediction of the text’s authenticity, with probabilities displayed below. The model is most reliable after a minimum of 50 tokens have been entered.. GPT-2 Output Detector enables users to quickly and accurately identify potential fake or fraudulent text inputs, and can be used for a variety of applications, such as detecting the authenticity of news articles or filtering out spam..
Content verification and plagiarism detection solution.
Detecting copied or generated content.
Authenticity detection for human & machine text.
Improving writing and ensuring academic integrity.
Forensically detects auto-generated text.
Analyzing sentences for written integrity verification.
Text sorts human and machine content.
Verification of content authenticity.
Simplifying the detection of AI-generated text.
Analyzes doctored media.
Authenticate and detect plagiarism in content.
Recognizes synthetic text.
Text plagiarism & source detect.
Badge indicating human-generated content.
Content proofing.
Detect machine vs human text for content management.
Detects ChatGPT- generated content plagiarism.
Plagiarism detection for educational institutions.
Identify and flag text for content verification.
Detecting plagiarism and verifying content.
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Detect AI generated text in a click
Detecting misinformation and deepfakes.
Classifies human vs machine text.
Differentiates human vs. synthetic text.
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Checked content's originality & plagiarism.
Reliable reviews by validated text.
Enhancing content authenticity via rewriting.
Evaluate & monitor generative models
Enhanced content creation and analysis for publishers.
Undetect humanizes & rewrites content.
Validated text content generation.
Differentiating human from generated text.
Detects generated text across domains.
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