ISAAC Text Classifiers
Coding-free demos for the classifiers built for the Illinois Social Attitudes (ISAAC) project. Pick a task below; each one loads its fine-tuned model on demand and offers a single-text tab and a bulk file tab.
- Relevance: is a text about a given social distinction (ability, age, race, sexuality, skin tone, weight)?
- Moralization: does a text frame its subject in moral terms?
- Generalization: how generalized vs. anecdotal is the language, clause by clause?
Held-out performance for every model is reported under Performance & citation, and each task tab repeats the figures for the model it runs.
Judges whether a text is relevant to a given social distinction, that is, whether it actually discusses people in terms of that attribute. Pick a distinction below; the app loads the matching fine-tuned classifier on demand. For race and skin tone, a 0.6 confidence threshold is applied to the "relevant" class (matching the ISAAC pipeline), so a text is only marked relevant when the model is sufficiently confident.
Which fine-tuned relevance classifier to apply.
By using this tool you agree to ISAAC's Use Agreement. For citation information and more details, see the Illinois Social Attitudes (ISAAC) repository.