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| title: ROTA App - Rapid Offense Text Autocoder | |
| emoji: ⚡️ | |
| colorFrom: blue | |
| colorTo: white | |
| sdk: streamlit | |
| sdk_version: 1.15.2 | |
| app_file: app.py | |
| pinned: true | |
| license: apache-2.0 | |
| # ROTA | |
| ## Rapid Offense Text Autocoder | |
| ### ℹ️ Intro | |
| [](https://huggingface.co/rti-international/rota) | |
| [](https://github.com/RTIInternational/rota) | |
| [](https://doi.org/10.5281/zenodo.4770492) | |
| Criminal justice research often requires conversion of free-text offense descriptions into overall charge categories to aid analysis. For example, the free-text offense of "eluding a police vehicle" would be coded to a charge category of "Obstruction - Law Enforcement". Since free-text offense descriptions aren't standardized and often need to be categorized in large volumes, this can result in a manual and time intensive process for researchers. ROTA is a machine learning model for converting offense text into offense codes. | |
| Currently ROTA predicts the *Charge Category* of a given offense text. A *charge category* is one of the headings for offense codes in the [2009 NCRP Codebook: Appendix F](https://www.icpsr.umich.edu/web/NACJD/studies/30799/datadocumentation#). | |
| The model was trained on [publicly available data](https://web.archive.org/web/20201021001250/https://www.icpsr.umich.edu/web/pages/NACJD/guides/ncrp.html) from a crosswalk containing offenses from all 50 states combined with three additional hand-labeled offense text datasets. | |
| For more information on the model, please see the [model repo](https://huggingface.co/rti-international/rota). | |
| This model and application were developed by the [RTI International Center for Data Science and AI](https://www.rti.org/centers/rti-center-data-science). |