| ## Lie Detector (RoBERTa) | |
| This model is a fine-tuned version of **roberta-base** on the **LIAR dataset**, a benchmark for political fact-checking introduced in ["Liar, Liar Pants on Fire"](https://arxiv.org/abs/1705.00648) (Wang, 2017). | |
| It classifies political statements into six categories: pants-fire, false, barely-true, half-true, mostly-true, true. | |
| Alongside the statement, the model uses: | |
| * Context and subjects | |
| * Metadata: speaker, party, state (as embeddings) | |
| * Numerical features**: historical counts of truthfulness | |
| ### Results | |
| * **Test Accuracy (six-way classification)**: 40.5% | |
| * **Original paper accuracy**: 27.4% | |
| ### Example | |
| ```python | |
| label = lie_detector( | |
| statement="We’ve added more jobs than any time in history.", | |
| subjects="economy,jobs", | |
| speaker_name="Joe Biden", | |
| speaker_title="President", | |
| state="delaware", | |
| party_affiliation="democrat", | |
| history_barely_true=14, | |
| history_false=12, | |
| history_half_true=24, | |
| history_mostly_true=21, | |
| history_pants_fire=5, | |
| context_location="CNN Town Hall" | |
| ) | |
| ``` |