IdeaLens
Collection
Detectors of whose ideas a document contains (IdeaLens) and who wrote it (ProseLens), plus their data. Code: github.com/RishanthRajendhran/IdeaLens • 16 items • Updated
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ProseLens-ModernBERT-L
ProseLens-ModernBERT-L is a prose-level detector: it reads the document itself and judges who wrote the words. It is the ModernBERT counterpart of ProseLens and the comparison point for the idea-level detectors.
| Model | ModernBERT-large with a sequence-classification head |
| Reads | the document text itself |
| Training data | WildOutlines, train split |
| Output | P(human); a document is flagged as AI when P(human) is below a cut |
| Default cut | 0.18790 (global, 1% false-positive rate) |
| Hardware | any GPU; a CPU works for small jobs |
Usage
Score documents directly; no outline is needed. The idealens package (PyPI) applies this model and the thresholds in this repo:
pip install "idealens[hf]"
idealens score docs.jsonl -o scores.jsonl --model ProseLens-ModernBERT-L
Input is JSONL with a text field per document.
In Python:
import idealens as il
with il.Detector("ProseLens-ModernBERT-L") as det:
records = det.score_documents([open("document.txt").read()])
print(records[0]["p_human"], records[0]["verdict"]["ai"])
Thresholds
thresholds.json holds this model's cuts at 0.1%, 0.5%, 1%, 2% and 5% false-positive rates, fitted on the 80,000 human
documents of WildOutlines' calibration split: one global cut per rate, plus per-format cuts. The package applies them. A cut
fitted for one model does not transfer to another model's scores. For documents unlike English web text, fit cuts on
human documents from your own domain with idealens calibrate.
Related
- IdeaLens: the main idea-level detector, with full documentation
- ProseLens: its prose-level counterpart
- idealens: the Python package that runs these models
- WildOutlines: the training corpus
- IdeaLens collection: every model and dataset in one place
Citation
@article{idealens2026,
title = {IdeaLens: Detecting AI Ideas in Long-form Writing},
author = {Anonymous},
journal = {arXiv preprint arXiv:TBD},
year = {2026},
url = {https://arxiv.org/abs/TBD}
}
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Model tree for rishanthrajendhran/ProseLens-ModernBERT-L
Base model
answerdotai/ModernBERT-large