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license: mit

widget:
- text: "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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  <img src="https://i.postimg.cc/HLqPqkyk/Central-Bank-Ro-BERTa-logos-black.png" width="200" height="200" style="margin-right: 20px;">
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    <h1 style="font-size: 36px; font-weight: bold; margin: 0;">CentralBankRoBERTa</h1>
    <p style="font-size: 18px; margin: 0;">A Fine-Tuned Large Language Model for Central Bank Communications</p>
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## CentralBankRoBERTa

CentralBankRoBERTA is a large language model. It combines an economic [agent classifier](Moritz-Pfeifer/CentralBankRoBERTa-audience-classifier) that distinguishes five basic macroeconomic agents with a binary sentiment classifier that identifies the emotional content of sentences in central bank communications.

#### Overview

The SentimentClassifier model is designed to detect whether a given sentence is positive or negative for either **households**, **firms**, **the financial sector** or **the government**. This model is based on the RoBERTa architecture and has been fine-tuned on a diverse and extensive dataset to provide accurate predictions.

#### Intended Use

The AudienceClassifier model is intended to be used for the analysis of central bank communications where sentiment analysis is essential. 

#### Performance

- Accuracy: 88%
- F1 Score: 0.88
- Precision: 0.88
- Recall: 0.88

### Usage

You can use these models in your own applications by leveraging the Hugging Face Transformers library. Below is a Python code snippet demonstrating how to load and use the AudienceClassifier model:

```python
from transformers import pipeline

# Load the AudienceClassifier model
audience_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier")

# Perform audience classification
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.")
print("Sentiment:", sentinement_result[0]['label'])
```

<table>
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    <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" ADD SOURCE/LINK
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    <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>
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    <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>
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