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---
license: mit
task_categories:
- text-classification
language:
- en
size_categories:
- 1K<n<10K
---
This dataset contains two manually pre-labeled datasets:
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.
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).
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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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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Moritz Pfeifer<br>
Institute for Economic Policy, University of Leipzig<br>
04109 Leipzig, Germany<br>
<a href="mailto:[email protected]">[email protected]</a>
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Vincent P. Marohl<br>
Department of Mathematics, Columbia University<br>
New York NY 10027, USA<br>
<a href="mailto:[email protected]">[email protected]</a>
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