--- 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 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'])