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README.md

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 license: mit
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+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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+## CentralBankRoBERTa
+
+CentralBankRoBERTA is a large language model. It combines an economic agent classifier that distinguishes five basic macroeconomic agents with a binary [sentiment classifier](Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier) that identifies the emotional content of sentences in central bank communications.
+
+#### Overview
+
+The AudienceClassifier model is designed to classify the target audience of a given text. It can determine whether the text is adressing **households**, **firms**, **the financial sector**, **the government** or **the central bank** itself. 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 content categorization based on target audiences is essential. 
+
+#### Performance
+
+- Accuracy: 93%
+- F1 Score: 0.93
+- Precision: 0.93
+- Recall: 0.93
+
+### 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-audience-classifier")
+
+# Perform audience classification
+audience_result = audience_classifier("We used our liquidity tools to make funding available to banks that might need it.")
+print("Audience Classification:", audience_result[0]['label'])