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--- |
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license: mit |
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widget: |
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- text: >- |
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We used our liquidity tools to make funding available to banks that might |
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need it. |
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datasets: |
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- Moritz-Pfeifer/CentralBankCommunication |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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- finance |
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--- |
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<div style="display: flex; align-items: center;"> |
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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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<div> |
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<h1 style="font-size: 36px; font-weight: bold; margin: 0;">CentralBankRoBERTa</h1> |
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<p style="font-size: 18px; margin: 0;">A Fine-Tuned Large Language Model for Central Bank Communications</p> |
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</div> |
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</div> |
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## CentralBankRoBERTa |
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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. |
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#### Overview |
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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. |
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#### Intended Use |
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The AgentClassifier model is intended to be used for the analysis of central bank communications where content categorization based on target agents is essential. |
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#### Performance |
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- Accuracy: 93% |
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- F1 Score: 0.93 |
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- Precision: 0.93 |
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- Recall: 0.93 |
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### Usage |
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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: |
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```python |
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from transformers import pipeline |
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# Load the AgentClassifier model |
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agent_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-agent-classifier") |
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# Perform agent classification |
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agent_result = agent_classifier("We used our liquidity tools to make funding available to banks that might need it.") |
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print("Agent Classification:", agent_result[0]['label']) |
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``` |
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<table> |
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<tr> |
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<td colspan="2" style="border-top: 1px solid #ccc; padding: 5px; text-align: left;"> |
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Please cite this model as Pfeifer, M. and Marohl, V.P. (2023) "CentralBankRoBERTa: A Fine-Tuned Large Language Model for Central Bank Communications" |
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</td> |
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</tr> |
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<tr> |
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<td style="padding: 5px;"> |
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Moritz Pfeifer<br> |
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Institute for Economic Policy, University of Leipzig<br> |
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04109 Leipzig, Germany<br> |
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<a href="mailto:pfeifer@wifa.uni-leipzig.de">pfeifer@wifa.uni-leipzig.de</a> |
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</td> |
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<td style="padding: 5px;"> |
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Vincent P. Marohl<br> |
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Department of Mathematics, Columbia University<br> |
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New York NY 10027, USA<br> |
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<a href="mailto:vincent.marohl@columbia.edu">vincent.marohl@columbia.edu</a> |
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</td> |
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</tr> |
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</table> |
