README.md

---
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."
---
## 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
sentinemnt_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:", sentinemnt_result[0]['label'])