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# Model Details
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Political DEBATE (DeBERTa Algorithm for Textual Entailment) is an NLI classifier trained for zero-shot and few-shot classification of political documents.
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[Zero-shot tutorial](https://colab.research.google.com/drive/1zi-8pMx_x-vo0m8XVmYVtw0Dhdw7vfFD#scrollTo=T4bo1aEjB9iG)
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[Few-shot tutorial](https://colab.research.google.com/drive/1Sv82jqRSwiIyuvEIDrhTiqF8_ClQaL0r#scrollTo=Uuaw8-qpn8S6)
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The model was trained using [Moritz Laurer's](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0-c) zero shot model as the foundation, and then trained further using the [PolNLI dataset.](https://huggingface.co/datasets/mlburnham/Pol_NLI) The model was trained for four classification categories:
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# Model Details
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Political DEBATE (DeBERTa Algorithm for Textual Entailment) is an NLI classifier trained for zero-shot and few-shot classification of political documents.
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[Zero-shot tutorial](https://colab.research.google.com/drive/1zi-8pMx_x-vo0m8XVmYVtw0Dhdw7vfFD#scrollTo=T4bo1aEjB9iG)
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[Few-shot tutorial](https://colab.research.google.com/drive/1Sv82jqRSwiIyuvEIDrhTiqF8_ClQaL0r#scrollTo=Uuaw8-qpn8S6)
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The model was trained using [Moritz Laurer's](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0-c) zero shot model as the foundation, and then trained further using the [PolNLI dataset.](https://huggingface.co/datasets/mlburnham/Pol_NLI) The model was trained for four classification categories:
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