GPT-2 Output Detector - AI Text Detector
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GPT-2 Output Detector Brief overview
GPT-2 Output Detector Demo (OpenAI OpenAI Detector) is a browser-based tool that estimates whether a piece of text is likely human-written (“Real”) or generated (“Fake”). It’s an online demo of OpenAI’s GPT‑2 output detector, implemented using a RoBERTa-based classifier, and it displays probabilities for each label after you paste in text. The tool notes that results become more reliable once your input is roughly 50+ tokens (i.e., more than a couple of sentences).
Under the hood, the detector is a fine-tuned RoBERTa base model trained to distinguish outputs of the 1.5B-parameter GPT‑2 model from human text (WebText). It’s best understood as a GPT‑2-era detector, not a universal “AI detector” for all modern models.
How to use
- Open the GPT‑2 Output Detector Demo in your web browser (hosted as a Hugging Face Space).
- Paste the text you want to check into the input box.
- Aim for at least ~50 tokens (a short paragraph or more) for more stable results.
- Review the displayed probabilities for “Real” vs “Fake.” Higher “Fake” generally indicates the model thinks the text resembles GPT‑2-generated output.
- Use the result as a signal, not a final verdict—especially for text that may come from newer LLMs or heavily edited content.
GPT-2 Output Detector Key features and functions
- Probability-based classification: Returns likelihood scores for “Real” and “Fake,” rather than only a binary label.
- Best for GPT‑2-style detection: Designed to predict whether text was generated by GPT‑2 models (trained on GPT‑2 1.5B outputs).
- Reliability guidance: Indicates results tend to be more meaningful after ~50 tokens.
- Research-oriented usage + limitations: The model card explicitly warns against using it as a ChatGPT misconduct detector, since it can be inaccurate for ChatGPT-like outputs and should not be used for serious accusations.
- Developer option (model reuse): The underlying model can be run via the Transformers “text-classification” pipeline for integration into internal workflows.
Pricing
- Free to use (as a public demo): The detector is available as a publicly accessible Hugging Face Space, and Hugging Face also offers a free Hub tier.
- Optional paid Hugging Face plans (platform-level): Hugging Face lists PRO at $9/month and Team at $20/user/month for added platform benefits (not required just to try the demo).
- Optional hosting/compute costs (if you deploy your own Space): Hugging Face Spaces hardware includes a FREE CPU Basic tier and paid upgrades (for example, CPU Upgrade is listed at $0.03/hour, with GPU options priced higher).
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