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license: mit |
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widget: |
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- 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 |
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CentralBankRoBERTA is a large language model. It combines an economic [agent classifier](Moritz-Pfeifer/CentralBankRoBERTa-audience-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 SentimentClassifier model is designed to detect whether a given sentence is positive or negative for either **households**, **firms**, **the financial sector** or **the government**. 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 AudienceClassifier model is intended to be used for the analysis of central bank communications where sentiment analysis is essential. |
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#### Performance |
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- Accuracy: 88% |
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- F1 Score: 0.88 |
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- Precision: 0.88 |
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- Recall: 0.88 |
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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-sentiment-classifier") |
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# Perform audience classification |
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sentinemnt_result = audience_classifier("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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print("Sentiment:", sentinemnt_result[0]['label']) |
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