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README.md
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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
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#### Overview
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The
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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
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#### Performance
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- Accuracy:
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- F1 Score: 0.
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- Precision: 0.
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- Recall: 0.
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### Usage
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from transformers import pipeline
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# Load the AudienceClassifier model
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audience_classifier = pipeline("
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# Perform audience classification
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print("
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---
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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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from transformers import pipeline
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# Load the AudienceClassifier model
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audience_classifier = pipeline("sentiment-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 Classification:", sentinemnt_result[0]['label'])
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