| --- |
| license: cc-by-nc-sa-4.0 |
| base_model: answerdotai/ModernBERT-large |
| library_name: transformers |
| language: |
| - en |
| tags: |
| - ai-text-detection |
| - prose-provenance |
| datasets: |
| - rishanthrajendhran/WildOutlines |
| --- |
| |
| # 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](https://huggingface.co/rishanthrajendhran/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](https://huggingface.co/datasets/rishanthrajendhran/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](https://github.com/RishanthRajendhran/IdeaLens) package ([PyPI](https://pypi.org/project/idealens/)) applies this model and the thresholds in this repo: |
|
|
| ```bash |
| 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: |
|
|
| ```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](https://huggingface.co/rishanthrajendhran/IdeaLens): the main idea-level detector, with full documentation |
| - [ProseLens](https://huggingface.co/rishanthrajendhran/ProseLens): its prose-level counterpart |
| - [idealens](https://github.com/RishanthRajendhran/IdeaLens): the Python package that runs these models |
| - [WildOutlines](https://huggingface.co/datasets/rishanthrajendhran/WildOutlines): the training corpus |
| - [IdeaLens collection](https://huggingface.co/collections/rishanthrajendhran/idealens-6abee785ce6196fc0be9200f): every model and dataset in one place |
|
|
| ## License |
|
|
| [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/): free to share and adapt for non-commercial |
| purposes, with attribution and under the same license. Built on [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) (Apache 2.0). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{idealens2026, |
| title = {IdeaLens: Detecting AI Ideas in Long-form Writing}, |
| author = {Rajendhran, Rishanth and Choi, Minjoon and Russell, Jenna and Namuduri, Ramya and B{\"o}l{\"o}ni-Turgut, Deniz and Karpinska, Marzena and Wieting, John and Iyyer, Mohit}, |
| journal = {arXiv preprint arXiv:2610.06778}, |
| year = {2026}, |
| eprint = {2610.06778}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL}, |
| url = {https://arxiv.org/abs/2610.06778} |
| } |
| ``` |
|
|
