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license: mit |
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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 that identifies the emotional content of sentences in central bank communications. |
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#### Overview |
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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 a state-of-the-art deep learning 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 AudienceClassifier model is intended to be used in various applications where content categorization based on target audiences 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 AudienceClassifier model: |
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```python |
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from transformers import pipeline |
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# Load the AudienceClassifier model |
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audience_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-audience-classifier") |
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# Perform audience classification |
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audience_result = audience_classifier("Your text goes here.") |
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print("Audience Classification:", audience_result[0]['label']) |
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