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--- |
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license: mit |
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task_categories: |
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- text-classification |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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--- |
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This dataset contains two manually pre-labeled datasets: |
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In the **economic agents dataset**, we labeled 6,205 randomized sentences from a [Fed database](https://github.com/Moritz-Pfeifer/CentralBankRoBERTa/tree/main/Data/FED) containing speeches (1948-2023) as speaking either about households, firms, the financial sector, the government, or the central bank itself. |
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In the **sentiment dataset**, we labeled 6,683 randomized sentences from the same database, which are either labeled as being positive (1) or negative (0). |
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The datasets were used to train an [agent classifier](https://huggingface.co/Moritz-Pfeifer/CentralBankRoBERTa-agent-classifier) and a [sentiment classifier](https://huggingface.co/Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier). |
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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:[email protected]">[email protected]</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:[email protected]">[email protected]</a> |
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</td> |
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</tr> |
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</table> |
