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Update README.md
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README.md
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@@ -36,3 +36,26 @@ audience_classifier = pipeline("text-classification", model="Moritz-Pfeifer/Cent
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# Perform audience classification
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sentinement_result = audience_classifier("The early effects of our policy tightening are also becoming visible, especially in sectors like manufacturing and construction that are more sensitive to interest rate changes.")
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print("Sentiment:", sentinement_result[0]['label'])
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# Perform audience classification
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sentinement_result = audience_classifier("The early effects of our policy tightening are also becoming visible, especially in sectors like manufacturing and construction that are more sensitive to interest rate changes.")
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print("Sentiment:", sentinement_result[0]['label'])
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```
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<table>
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<tr>
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<td colspan="2" style="border-top: 1px solid #ccc; padding: 5px; text-align: left;">
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Please cite this model as Pfeifer, M. and Marohl, V.P. (2023) "CentralBankRoBERTa: A Fine-Tuned Large Language Model for Central Bank Communications" ADD SOURCE/LINK
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</td>
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</tr>
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<tr>
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<td style="padding: 5px;">
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Moritz Pfeifer<br>
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Institute for Economic Policy, University of Leipzig<br>
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04109 Leipzig, Germany<br>
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<a href="mailto:pfeifer@wifa.uni-leipzig.de">pfeifer@wifa.uni-leipzig.de</a>
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</td>
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<td style="padding: 5px;">
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Vincent P. Marohl<br>
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Department of Mathematics, Columbia University<br>
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New York NY 10027, USA<br>
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<a href="mailto:vincent.marohl@columbia.edu">vincent.marohl@columbia.edu</a>
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</td>
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</tr>
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</table>
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