BERT classifier that scores whether academic text reads as human or AI-written, run locally with Python.
A BERT model on Hugging Face that labels academic paragraphs as human or AI style.
Open Detector is a BERT-base binary classifier for telling human academic writing from AI-written style. It suits students, researchers and technical users who want a local, inspectable alternative to commercial paper checkers.
The repo provides a model hosted on Hugging Face (followsci/bert-ai-text-detector) and a short Python snippet that returns AI and human percentages for a paragraph. The README says it is stylometric detection only, not a check of content truth.
It suits people checking academic prose who can run Python. It does not offer a web interface or sentence highlighting in the repository, and the README points to a separate hosted platform for those.
Binary human-versus-AI model fine-tuned on about 1.4 million samples, per the README.
Trained and described for academic writing patterns, not general web copy.
Weights are downloaded from followsci/bert-ai-text-detector; the model card states MIT.
pip install transformers torch, then score a paragraph in a few lines.
README claims a 0.82% false-positive rate on its own dataset.
README is provided in English and Chinese.
Open Detector is free under the MIT licence. The README also links a separate hosted platform, XYZ SCIENCE, with free detection; otherwise the only cost is running the model yourself.
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The README positions it as an open alternative to commercial detectors such as Turnitin for AI-style detection, but it does no plagiarism matching.
Yes after the model is downloaded; the quick start uses only transformers and torch on your own machine.
No. The README calls it stylometric detection and argues that writing like AI is not misconduct.
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