avasil02/automated-humanizer-detector

Automated Humanizer — academic AI-text detector

RoBERTa sequence classifier (human vs. AI-generated) fine-tuned on academic prose. It is the default hf detector backend of the automated_humanizer pipeline, where it gates a humanize → detect → validate rewrite loop.

• —Architecture: RobertaForSequenceClassification, 2 labels
• —Training data: synthetic (AI-draft, human-original) paragraph pairs built from award-winning pre-2020 arXiv papers (see the project's training/ subsystem)

Honest limitation

This detector was trained on the same data family as the project's humanizer, so its confidence is an internal, biased estimate — useful as an optimization signal inside the loop, not as independent evidence of authorship. The project README recommends an external detector (e.g. GPTZero) for final sign-off, and test_detectors.py in the repo reproduces why.

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="avasil02/automated-humanizer-detector")
print(clf("Paragraph of academic text..."))

Check the human-class label index once per deployment with the project's calibrate_detector.py.