--- license: mit widget: - text: >- We used our liquidity tools to make funding available to banks that might need it. datasets: - Moritz-Pfeifer/CentralBankCommunication language: - en pipeline_tag: text-classification tags: - finance ---

CentralBankRoBERTa

A Fine-Tuned Large Language Model for Central Bank Communications

## CentralBankRoBERTa CentralBankRoBERTA is a large language model. It combines an economic agent classifier that distinguishes five basic macroeconomic agents with a binary [sentiment classifier](https://huggingface.co/Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier) that identifies the emotional content of sentences in central bank communications. #### Overview The AgentClassifier model is designed to classify the target agent 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 AgentClassifier model is intended to be used for the analysis of central bank communications where content categorization based on target agents 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 AgentClassifier model: ```python from transformers import pipeline # Load the AgentClassifier model agent_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-agent-classifier") # Perform agent classification agent_result = agent_classifier("We used our liquidity tools to make funding available to banks that might need it.") print("Agent Classification:", agent_result[0]['label']) ```

Please cite this model as Pfeifer, M. and Marohl, V.P. (2023) "CentralBankRoBERTa: A Fine-Tuned Large Language Model for Central Bank Communications"
Moritz Pfeifer Institute for Economic Policy, University of Leipzig 04109 Leipzig, Germany pfeifer@wifa.uni-leipzig.de	Vincent P. Marohl Department of Mathematics, Columbia University New York NY 10027, USA vincent.marohl@columbia.edu