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--- |
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license: mit |
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language: |
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- en |
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--- |
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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. |
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This model was trained specifically for additional analyses presented in this [paper](https://doi.org/10.48550/arXiv.2405.07323). |
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## Model performance |
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The model performance on unseen test set is as follows: |
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<div align="center"> |
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| Label | F1 score | |
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|:----------------------|-----------:| |
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| Macroeconomics | 0.8303 | |
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| Civil rights | 0.7676 | |
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| Health | 0.8886 | |
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| Agriculture | 0.8439 | |
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| Labor | 0.7818 | |
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| Education | 0.9005 | |
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| Environment | 0.8481 | |
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| Energy | 0.8629 | |
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| Immigration | 0.8682 | |
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| Transportation | 0.8731 | |
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| Law and crime | 0.8207 | |
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| Social welfare | 0.7957 | |
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| Housing | 0.8462 | |
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| Domestic commerce | 0.8421 | |
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| Defense | 0.8627 | |
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| Technology | 0.8333 | |
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| Foreign trade | 0.8269 | |
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| International affairs | 0.8907 | |
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| Government operations | 0.8777 | |
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| Public lands | 0.8758 | |
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| Others | 0.6543 | |
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| **Macro average** | **0.8573** | |
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</div> |
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## Citation |
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If you find this model useful for your work, please consider citing: |
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```bibtex |
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@article{aroyehun2024computational, |
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title={Computational analysis of US Congressional speeches reveals a shift from evidence to intuition}, |
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author={Aroyehun, Segun Taofeek and Simchon, Almog and Carrella, Fabio and Lasser, Jana and Lewandowsky, Stephan and Garcia, David}, |
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journal={arXiv preprint arXiv:2405.07323}, |
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year={2024} |
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} |
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``` |
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