Update README.md

README.md

@@ -14,7 +14,7 @@ widget:
 
 ## CentralBankRoBERTa
 
-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.
+CentralBankRoBERTA is a large language model. It combines an economic [agent classifier](Moritz-Pfeifer/CentralBankRoBERTa-agent-classifier) that distinguishes five basic macroeconomic agents with a binary sentiment classifier that identifies the emotional content of sentences in central bank communications.
 
 #### Overview
 
@@ -22,7 +22,7 @@ The SentimentClassifier model is designed to detect whether a given sentence is
 
 #### Intended Use
 
-The AudienceClassifier model is intended to be used for the analysis of central bank communications where sentiment analysis is essential. 
+The AgentClassifier model is intended to be used for the analysis of central bank communications where sentiment analysis is essential. 
 
 #### Performance
 
@@ -33,16 +33,16 @@ The AudienceClassifier model is intended to be used for the analysis of central
 
 ### 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 AudienceClassifier model:
+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 SentimentClassifier model
-audience_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier")
+agent_classifier = pipeline("text-classification", model="Moritz-Pfeifer/CentralBankRoBERTa-sentiment-classifier")
 
 # Perform sentiment analysis
-sentinement_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.")
+sentinement_result = agent_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.")
 print("Sentiment:", sentinement_result[0]['label'])
 ```