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@@ -13,7 +13,7 @@ co2_eq_emissions: 1.06099358268878
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  This model aims at predicting the nature of a sentence in a French political sentence.
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  The predictions fall in three categories:
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  - `problem`: the sentence describes a problem (usually to be tackled by the speaker), for example _il y a dans ce pays une fracture_ (J. Chirac)
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- - `solution`: the sentences describes a solution (typically part of a political programme), for example: _on va donc nettoyer, au propre comme au figuré, la cité des 4000_ (N. Sarkozy)
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  - `other`: the sentence does not belong to any of these categories, for example: _vive la République, vive la France_
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  This model was trained using AutoNLP based on sentences extracted from a mix of political tweets and speeches.
 
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  This model aims at predicting the nature of a sentence in a French political sentence.
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  The predictions fall in three categories:
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  - `problem`: the sentence describes a problem (usually to be tackled by the speaker), for example _il y a dans ce pays une fracture_ (J. Chirac)
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+ - `solution`: the sentences describes a solution (typically part of a political programme), for example: _J’ai supprimé les droits de succession parce que je crois au travail et parce que je crois à la famille._ (N. Sarkozy)
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  - `other`: the sentence does not belong to any of these categories, for example: _vive la République, vive la France_
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  This model was trained using AutoNLP based on sentences extracted from a mix of political tweets and speeches.