---
license: mit
language:
- en
---
This is a transformers model trained on the U.S. Comparative Agendas Project (CAP) dataset, annotated with a top-level taxonomy covering 20 policy areas, as well as an "Others" category for non-policy-related text. The model is designed to identify policy and non-policy issues in political discourse.

This model was trained specifically for additional analyses presented in this [paper](https://doi.org/10.48550/arXiv.2405.07323).



## Model performance

The model performance on unseen test set is as follows:

<div align="center">
  
| Label                 |   F1 score |
|:----------------------|-----------:|
| Macroeconomics        |     0.8303 |
| Civil rights          |     0.7676 |
| Health                |     0.8886 |
| Agriculture           |     0.8439 |
| Labor                 |     0.7818 |
| Education             |     0.9005 |
| Environment           |     0.8481 |
| Energy                |     0.8629 |
| Immigration           |     0.8682 |
| Transportation        |     0.8731 |
| Law and crime         |     0.8207 |
| Social welfare        |     0.7957 |
| Housing               |     0.8462 |
| Domestic commerce     |     0.8421 |
| Defense               |     0.8627 |
| Technology            |     0.8333 |
| Foreign trade         |     0.8269 |
| International affairs |     0.8907 |
| Government operations |     0.8777 |
| Public lands          |     0.8758 |
| Others                |     0.6543 |
| **Macro average**         |     **0.8573** |

</div>


## Citation
If you find this model useful for your work, please consider citing:
```bibtex
@article{aroyehun2024computational,
  title={Computational analysis of US Congressional speeches reveals a shift from evidence to intuition},
  author={Aroyehun, Segun Taofeek and Simchon, Almog and Carrella, Fabio and Lasser, Jana and Lewandowsky, Stephan and Garcia, David},
  journal={arXiv preprint arXiv:2405.07323},
  year={2024}
}
```