[![Paper Page](https://img.shields.io/badge/Paper_Page-j.jfds.2023.100114-green)](https://doi.org/10.1016/j.jfds.2023.100114) 

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). 

<table>
  <tr>
    <td colspan="2" style="border-top: 1px solid #ccc; padding: 5px; text-align: left;">
      Please cite this model as Pfeifer, M. and Marohl, V.P. (2023) "CentralBankRoBERTa: A Fine-Tuned Large Language Model for Central Bank Communications". <em>Journal of Finance and Data Science </em> (forthcoming) <a href="https://doi.org/10.1016/j.jfds.2023.100114">https://doi.org/10.1016/j.jfds.2023.100114</a> 
    </td>
  </tr>
  <tr>
    <td style="padding: 5px;">
      Moritz Pfeifer<br>
      Institute for Economic Policy, University of Leipzig<br>
      04109 Leipzig, Germany<br>
      <a href="mailto:pfeifer@wifa.uni-leipzig.de">pfeifer@wifa.uni-leipzig.de</a>
    </td>
    <td style="padding: 5px;">
      Vincent P. Marohl<br>
      Department of Mathematics, Columbia University<br>
      New York NY 10027, USA<br>
      <a href="mailto:vincent.marohl@columbia.edu">vincent.marohl@columbia.edu</a>
    </td>
  </tr>
</table>

### BibTeX entry and citation info

```bibtex
@article{Pfeifer2023,
  title = {CentralBankRoBERTa: A fine-tuned large language model for central bank communications},
  journal = {The Journal of Finance and Data Science},
  volume = {9},
  pages = {100114},
  year = {2023},
  issn = {2405-9188},
  doi = {https://doi.org/10.1016/j.jfds.2023.100114},
  url = {https://www.sciencedirect.com/science/article/pii/S2405918823000302},
  author = {Moritz Pfeifer and Vincent P. Marohl},
}
```

task_categories:text-classification size_categories:1K<n<10K language:en license:mit region:us
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"]}
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #region-us 

![Paper Page](URL


This dataset contains two manually pre-labeled datasets:


In the economic agents dataset, we labeled 6,205 randomized sentences from a Fed database 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 and a sentiment classifier.



### BibTeX entry and citation info



### BibTeX entry and citation info
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #region-us 
 ### BibTeX entry and citation info
passage: TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #region-us 
### BibTeX entry and citation info